best machine learning projects for resume

15+ Machine Learning Projects for Resume with Source Code

Machine learning projects for resume that you can add to show how your machine learning skills and experiences fit into the ML job role you're applying for.

Sending out the exact old traditional style data science or machine learning resume might not be doing any favours in your machine learning job search. With cut-throat competition in the industry for high-paying machine learning jobs, a boring cookie-cutter resume might not just be enough. What if we told you there is a simple addition to your machine learning engineer resume to increase your chances of landing a lucrative ML engineer job. 

You would add it in a jiffy, right?

Well, yes, there is. All you need to do is highlight different types of machine learning projects on your resume. 

The best way to showcase you have the required machine learning skills is to highlight how you’ve mastered those skills practically.

Classification Projects on Machine Learning for Beginners - 1

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Table of Contents

Machine learning projects for resume - a must-have to get hired in 2023, machine learning projects for resume - the different types to have on your cv, machine learning project ideas for resume, how to list machine learning projects on resume, faqs on machine learning projects for resume.

Machine Learning and Data Science have been on the rise in the latter part of the last decade. Thanks to innovation and research in machine learning algorithms, we can seek knowledge and learn from insights that hide in the data. Data Engineers, Data Scientists, Data Architects have become significant job titles in the market, and the opportunities keep soaring.

Machine Learning Trends in Recent Years

Deep Learning Trends in Recent Years

With the global machine learning job market projected to be worth $31 billion by the end of 2024 and fierce competition in the industry, a machine learning project portfolio is a must-have. We’ve compiled a list of machine learning projects for a resume to help engineering students or anyone wanting to pursue a machine learning career stand out like GitHub Copilot in the interview. A strong machine learning resume includes different types of machine learning projects. What’s better, we have categorised them into different types, so you can include one project of each type to upgrade your resume with a versatile machine learning skillset for your next ML job interview . Every domain of machine learning presents its challenges and solutions. Hence, having diverse types of machine learning projects for your resume helps recruiters understand your problem-solving approach to various business problems.

A typical machine learning project involves data collection, data cleaning, data transformation, feature extraction, model evaluation approaches to find the best model fitting and hyper tuning parameters for efficiency. Building an ML project from scratch ensures understanding of every step in the machine learning project lifecycle . 

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The ML project types listed below are not exhaustive. Still, they cover diverse types of machine learning projects that can add value to a resume and also one should get hands-on practice before appearing for any data science or machine learning job interview.

1. Machine Learning Projects on Classification

Classification refers to labelling groups of data into known categories or classes. Having ML projects on classification listed on your resume help hiring managers to understand your skills on how to tackle any classification problem end-to-end and select the suitable classification machine learning algorithm . Quite similar to classification is clustering but with the minor difference of working with unlabelled data. Clustering defines the process of grouping together identical objects into individual clusters. So, you can add both classification and clustering related machine learning projects to your resume. 

2. Machine Learning Projects on Prediction 

Predictive modelling often uses historical data to learn and predict the likelihood of an event in the future. Historical data provides insights and patterns for making valuable business predictions—for example, predicting customer churn for an organisation in the next 30 days. Having prediction machine learning projects will help hiring managers to understand how the predictions made by an ML model you built can help organisations take action on a product or a service.

3. Machine Learning Projects on Computer Vision 

Working on hands-on ML projects that employ machine learning algorithms like OpenCV, VGG, ResNet to make sense of real-world objects and environments will show your abilities on how you handle diverse computer vision tasks using machine learning. Some examples of such problems include real-time fruit detection , face recognition , self-driving cars, etc. 

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4. Machine Learning Projects on Natural Language Processing (NLP)

NLP helps computers understand, analyse, and process human language to derive meaningful insights from it. Recognising handwritten letters, speech recognition, text summarization , chatbots, etc, are some projects that you can build to showcase your NLP skills. NLP projects are a treasured addition to your arsenal of machine learning skills as they help highlight your skills in really digging into unstructured data for real-time data-driven decision making.

5. Deep Learning and Neural Network Projects 

Deep learning is a subset of machine learning and one of the most hyped machine learning techniques today. Add deep learning and neural network projects to your resume if you want to showcase your advanced machine learning skills.  Adding deep learning projects to your resume is not a must-have if you’re applying for entry-level machine learning job roles, but they are good to have. 

6. Machine Learning Projects on Time Series Forecasting 

Time series analysis and forecasting is a crucial part of machine learning that engineering students often neglect. Adding machine learning projects from time-series data is an important machine learning skill to have on your resume. Usually, the time element in the data has valuable information for a machine learning model to glean insights, but at times, it could lead to insights that might not be real. Showcasing time-series projects on your resume will highlight your ability to identify the challenges associated with working with time series data and how you tackle those challenges before it’s too late.

Top 30 Machine Learning Projects for Beginners 

New Projects

Let's delve into the different types of ML project ideas in more detail.

1) Machine Learning Projects for Resume on Classification

Classification in machine learning is a technique that classifies data into selected classes or labels. Syntactically or semantically similar data form one particular class. The classes are referred to as collections, labels or targets as well. A typical classification problem is to identify the class for a given data point or instance.  

The principle behind classification problems is to feed large amounts of data to the model and check for prediction accuracy using supervised learning. The idea is to try multiple models and assess the best-suited algorithm for the problem. Since real-world problems are peculiar and characteristic, it is imperative to check for different models before deciding which machine learning model best fits a given use case. 

Machine Learning Project Ideas for Classification Problems

Sentiment Analysis ML Project for Product Reviews 

Sentiment Analysis is the process of identifying the emotions/sentiment in a text. Companies commonly use it to infer social media reviews, customer response and brand reputation. Sentiment analysis segregates the core of the sentiments into four main categories. 

Polarity is the tonality of the text—Ex, negative, positive or neutral.

Emotion signifies happiness, sadness, anger or confusion. 

Urgency means the graveness and criticality of the text, namely urgent or not urgent.

Intention infers whether a customer is interested or not interested.

Pairwise Ranking and Sentiment Analysis of Customer Reviews

The data set for the project contains over 1600 product reviews for medical products, which have been labelled as informative and non-informative. The project’s goal is to perform sentiment analysis on the reviews and rank them in their order of relevance. We start with preprocessing and cleaning the data which is sent to the feature extraction module. After the features are collected and vectorised, we proceed with the classification. Random Forest algorithm is used and performs reasonably well with an accuracy of 85 per cent and above. Finally, pair-wise ranking is done for each review against every other review.

E-Commerce Review Sentiment Analysis Project with Guided Videos

Building Recommender Systems

Recommender systems suggest similar items, places, movies, objects based on a person’s personality, preferences, and likings. Behind the scenes, it groups people with similar tastes together and recommends items from their collective repertoire. Recommender systems are widespread in the industry, with massive applications in eCommerce (Amazon), media(Netflix), and financial institutions (PwC). Content-based filtering and collaborative filtering are the most common techniques employed in the implementation of recommender systems.

Evolution of Machine Learning Applications in Finance : From Theory to Practice

Music Recommendation System on KKbox Dataset

The project aims at predicting if a user will listen to a song again in a period.

KKbox provides a dataset for the project in user-song pairs and the first recorded listening time, along with song and user details. Outliers in the dataset are dropped, and null values are imputed.  The XgBoost algorithm is used to predict the chance of relistening with the highest accuracy. 

Music Recommendation System Project with Guided Videos 

Spam Email Filtering

Spam mail classification labels suspected emails as spam and stop the mails from reaching the mailbox. It checks for the mail content, specific signatures and suspicious patterns to learn about spam mails. There are many techniques available to filter out spam emails -

Content-Based Filtering

Content-Based Filtering creates automatic filtering rules by analysing words, the occurrence of specific words and phrases in the mail. 

Rule-Based Filtering

Rule-Based Filtering uses already created rules to score the message in the text by comparing the regular expression. If a text matches a certain number of threshold rules, it is tagged and spam, thus dropped. The rules are updated periodically to keep up with the variety and novelty in spam messages.

Case-Based Filtering

Case-Based Filtering is among the more popular filtering techniques where spam and non-spam emails are added to a dataset. The dataset goes through the preprocessing stage, and all the emails are converted to two vector classes, spam and nonspam. Learning algorithms are applied to the vectors to classify them as spam and non-spam emails. And finally, testing for new mails occurs on the model. 

Adaptive Spam Filtering

Adaptive Spam Filtering classifies spam emails into various classes. The complete email dataset is divided into groups with emblematic signatures—the algorithm checks for similarities between the incoming mails and the groups and classifies the mails accordingly. 

You can use the day to day email exchanges that are tagged as spam and not spam as the dataset for this ML project idea. The data goes through preprocessing steps like stop words removal and vectorisation, which return the data set in a vector form ready for modelling. The model trains using Logistic regression with an accuracy upwards of 90 per cent. An output class of 1 means that the mail is spam where zero signifies not-spam and one as spam.  Another popular classification algorithm called Naive Bayes Classifier also provides good accuracy. 

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2) Machine Learning Projects for Resume on Prediction 

A prediction problem in machine learning is the most common, at par in occurrence with classification problems. Predictions take historical data and find the insights and trends hidden in the dataset .  The larger the dataset we have for training, the better and more accurate the prediction algorithm becomes. The current and historical data is taken to build a model that can predict events or trends in the future. The predictions can range from the potential risk of a credit card issue request to calculating the stock prices for a multinational company.

Machine Learning Project Ideas on Prediction Problems 

Sales forecasting.

Forecasting future sales depends on many factors like past sales, seasonal offers, holidays and festivals etc. Future sales also dictate staff requirements and stocking product inventory for future needs. Autoencoders and multivariate models can make a good fit for forecasting prediction problems where time is an added constraint. 

Rossmann Store Sales Prediction Project

The dataset contains historical data from more than 1000 Rossmann drug stores, including customer id, sales, store, state holidays, etc. Missing data points are imputed, and outliers get removed. Data is converted into numerical form by using one-hot encoding for easier manipulation. Stochastic Gradient Descent and Decision Tree regressor algorithms are mainly used in the model. 

Project to Forecast Future Sales of Rossman Store with Guided Video

Weather Forecasting

We tend to look at the weather report multiple times in our daily life. Predicting rainfall is of utmost importance to industries that depend on rains like agriculture . Weather predictions are relatively challenging and better done using Deep Learning algorithms . Even so, traditional ensemble models can offer outstanding results with the need for high resources. 

The project’s data set is featured at Kaggle with information on the date, the average temperature on land and sea, minimum and maximum temperature on land and sea. The previous value replaces null values in the dataset, and date entries are converted to a DateTime object. The Zero-differentiated ARIMA model is used for prediction as, along with being a prediction problem, weather forecasting is also a time series problem. Finally, the accuracy is measured by Akaike Information Criterion. 

Customer Churn Prediction

Customer churn is the behaviour of customers to stop using an organisation’s products or services. Customer churn rate is the rate of people who discontinue paid services in a particular interval of time. Churn is bad for companies as they lose revenue. Churn prediction finds applications in telecom, music and movie streaming services or other subscription-based services. Churn also signifies the health and reputation in the market for a company.

Customer Churn Prediction Analysis for Bank Records

The dataset from the bank records stores customer name, credit score, geography, balance, tenure, gender, etc. Preprocessing, imputing and label encoding are the next steps that occur. The dataset goes through feature extraction at this stage, eliminating less essential fields, making the dataset manageable and more consistent. The Light Gradient Boost Machine or LGBM algorithm provides maximum accuracy and is preferred for this project. Being lightweight, it is suitable in the big production setting of a bank.

Customer Churn Prediction Project with Guided Videos  

3) Machine Learning Projects for Resume on Computer Vision  

Computer Vision combines machine learning with image/vision analysis to enable systems to infer insights from videos and images. For a computer, it becomes quite a challenge to interpret pictures and distinguish the features. While as humans, we have evolved over a long time with our vision as a central characteristic. For humans, using vision to recognise objects around us is almost second nature. Computer Vision offers the possibility for computers to develop the vision to assimilate and comprehend the world around them. 

The main principle involved in computer vision is to break the image into pixels. Pixels are the most fundamental constituents of an image. By recognising the pattern in the pixel pool, computers begin the task of image identification. Equally important is what features we extract from these pixels and how we construct the learning model.

Computer Vision Techniques

Object Detection is the identification of objects in an image. These objects can be any person, thing, animal, or place but need distinctive features that the model uses to recognise and detect the subjects in the photos. Object detection happens through localisation, where a bounding box outlines the object. The object comprises many pixels, and those pixels belong to the same object class. Object detection is used in google photos, where google detects faces from our library of images.

Object Tracking refers to following the path of a particular object in a situation or environment. Stacked Auto Encoders (SAE) and Convolutional Neural Network  Surveillance is an ideal example of object tracking. 

Image Classification is tagging images under a class holding similar photos. An example of image classification is the annoying ‘Not a Robot’ authentication that forces one to select all the traffic lights in the image. 

Image Segmentation

Image segmentation aims to break the image into partitions or segments so that it’s easier to analyse and process the whole picture. There are two types of image segmentation possible, listed as follows:

Instance Segmentation - It recognises each object of the same type as a new object. So an image of three elephants would be categorised into three separate elephant classes, namely, elephant1, elephant2 and elephant3.

Semantic Segmentation - It understands the semantics in the pixels and labels semantically similar objects in the same class. Considering the elephant example from above, pixels in the image of three elephants will get tagged under only one elephant class, namely elephant.

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Machine Learning Project Ideas on Computer Vision

Face recognition.

Face recognition is a non-trivial computer vision problem that recognises faces and clusters them under appropriate classes. Face recognition finds uses in mobile phone applications, surveillance, photo tagging applications, google lens, etc. OpenCV is the most popular library that helps with building models for face recognition. 

Face Recognition System in Python using FaceNet

The dataset for the project is a video from the famous sitcom show called Friends. Frames per second from the video are extracted to form the dataset in which we need to recognise the cast’s faces. A total of 35 images, with seven images for each character, are collected. Haar Cascade Object is used for face detection and extraction, while Convolution Neural Network is used for model training. 

Face Recognition Project using Facenet with Guided Videos

Building an OCR System from Scratch 

Optical character recognition is the technique of identifying the letters and digits in a handwritten document or bill. It extracts the relevant information from the documents and records it in the database. Since handwritings come in numerous styles, OCR needs extensive training and fine-tuning of parameters. 

Building OCR in Python using YOLO and Tesseract

The dataset for the project is created using the Labellmg tool in python to label all the invoices present. After the labelling, we proceed with the YOLOv4 ( you only look once ) algorithm to detect the invoice number, date and total bill amount. Next, Tesseract is used to read/predict text from the detected fields.  We also use image augmentation to expand the dataset to a considerable size if the dataset is small. 

OCR Project Built from Scratch with Guided Videos 

Image Restoration - Denoising Images 

Image restoration is the reconstruction of old images to make them new-like with optimum quality and features. It takes into consideration both spatial information and frequency to replace missing values in a snap. 

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4) Machine Learning Projects for Resume on NLP 

Natural Language Processing is part of machine learning that involves understanding and processing human language, text, and spoken.  Here is a list of prevalent NLP tasks that will help in getting a sense of its wide array of applications:

Speech Recognition

Part of Speech Tagging

Word Sense Disambiguation

Named Entity Recognition

Coreference Resolution

Sentiment Analysis

Natural Language Generation 

NLP Techniques

Natural Language processing uses two effective techniques which differ in their approach to analysing language; they are namely:

• Remove Punctuation Punctuations clutter the data with useless tokens and don't add to the model efficiency. It's best practice to remove them beforehand. 
• Tokenisation is the breaking of sentences into smaller parts that can be either words or combination words. It makes data processing easier and uniform across the whole dataset.
• Lemmatisation is converting words to their most basic form called Lemma. The lemma replaces every other form of the word. For example, learning, learned, learnt, learnable shall be replaced with learning.
• Stemming is the process of dropping the beginnings and ends of words depending on their prefix and suffix. 
• Part of Speech Tagging labels tokens as a verb, adverb, adjective, noun etc., based on the grammatical vocabulary. It helps discern the difference between the noun and adjective forms of the same word if a comment has different meanings. For example, the word sense signifies the five senses and the act of perceiving. 
• Stop Words Removal focuses on deleting all the common stop words like a, an, the, and, like, just that don't add to the concrete meaning of the text.
• Vectorisation or Bag of Words is the process of counting the occurrences of individual words in a text. The count of each word helps in understanding how important the word is to the whole subtext.
• Word Sense Disambiguation identifies different forms/meanings of the exact words depending upon the context of its use and neighbouring terms.
• Word Relationship Extraction tried to infer the relationships between different words in a sentence like a place, subject, object etc.

Machine Learning Project Ideas on NLP

Build a chatbot.

Chatbots are NLP applications that enable us to query details and raise grievances in natural language to receive relevant information. Chatbots are prevalent in the customer service industry, where setting up call centres is cumbersome and not budget-friendly.  An example is the amazon chatbot that helps customers with order information, order cancellation etc. 

Natural Language Processing Chatbot using NLTK

The dataset is conversations from a leave enquiry and application system for the organisation. The textual data is pre-processed using various NLP techniques like lemmatisation, tokenisation, stemming and stop words removal. The occurrence of each word is counted to create a count vector model called a bag of words. You can use algorithms like Naive Bayes Classifier and Decision Tree for modelling. 

NLP Chatbot Project with Guided Videos

Speech Recognition 

Speech recognition is the ability of a machine to understand human language and respond coherently with appropriate data. Speech recognition finds use in our daily life while we use maps, call a friend or translate language all through our voice. Alexa in Amazon Echo and Siri in Apple iPhones are some of the best examples of speech recognition. 

Topic Modelling

Topic modelling is the inference of main keywords or topics from a large set of data. It measures the frequency of a word in the text and its relationship with neighbouring words to extract succinct information. Typical uses are labelling unstructured data into formatted topics. It can also be used in text summarisation problems with minor tweaks to the model. 

Topic Modelling using K-means Clustering on Customer Reviews 

The customer reviews for the project are sourced from Twitter for a particular company.

Data goes through many layers of preprocessing as the twitter reviews are unfiltered and raw. Tokenisation and vectorisation are performed using TD-IDF and count vectoriser. Model training is done using k-means clustering , an unsupervised learning algorithm. The final result is clusters of tweets with different classes that signify the dominant topic in the cluster. 

Topic Modelling for Customer Reviews ML Project with Source Code 

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5) Deep Learning and Neural Networks Projects for Resume

Deep Learning aims at mimicking and simulating human thought patterns by using complex and layered structures called Neural Networks . In simple terms, Deep learning is multiple Artificial Neural Networks connected. Neural networks can accomplish clustering, classification and regression with greater efficiency than traditional machine learning algorithms. 

Deep Learning eliminates the feature extraction process and skips over this step essential to all the traditional machine learning algorithms. These classic algorithms, called flat algorithms , cannot use data without preprocessing or feature extraction. Feature extraction is a detailed and involved process that needs expertise in the problem domain and patience in refining it over time. Deep Learning straight away discards this step and moves on with raw data. Deep Learning can learn and model the problem satisfactorily upon many iterations by tuning the weights using loss functions. 

A brief look at the architecture of a deep learning model

Nodes - A neural network is a collection of primary cells called Nodes. A Node stores arithmetic values like 0.4,2.21 etc

Weights are the branches that connect two nodes. They represent a number that keeps changing over the training time. The process of starting from a set of random weights to arriving with specific values that fits the input data is called Learning.

Loss Function defines the difference between the prediction vector obtained from the Neural network and the actual output vector—the lesser the loss function value, the better the model. 

Back Propagation of Errors pushes the errors back towards the input layer. In the process, it keeps updating the weights in each hidden layer. The principle behind this is that the total error gradient in the output layer is the sum of individual error gradients at each point in the network. 

Gradient Descent is when the weights are tuned using the derivative of the loss function to improve the network. The idea is to bring the weights to a value that spawns the most accurate prediction. 

The nonlinear activation function is applied to the dot product of the previous hidden layer vector and weights connecting the two participating layers. 

A feature vecto r is the input vector that goes into the Neural Network through the input layer. It contains a vectorised form of the input.

Prediction vector is the vector form of the output that the neural network produces.

Input and Output layers - Input and output layers are a neural network’s first and last layers. 

Input Layer is a set of nodes that represent a data point in vector form. For example, for image recognition models, the input layer would be the vectorised version of the image pixels. 

Output Layer denotes the result of the Neural Network. It is again a set of nodes quite like the input layer, but these individual nodes represent the output classes of the problem. For example, in the image recognition problem, the output layer would be nodes corresponding to objects in the image like cars, sheep, women etc.

Hidden Layers are the layers sandwiched between the input and the output layer. All the computations ( like weights tuning ) happen among these layers.

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Deep Learning and Neural Network Project Ideas for Resume

Self-driving autonomous cars.

Autonomous-driving cars can navigate through traffic and control acceleration and speed depending on their environment. Perception, Localisation, Planning, control are the four central ideas in self-driving cars. 

Perception is figuring out the environment and obstacles. 

Planning is the trajectory from point A to point B

Localisation is identifying the current location in the world.

Control relates to steering angle and acceleration. 

Natural Language Translation using Deep Learning 

Language translation is extremely important in international trade, discourses, education and media where two parties interact without any common language. It translates text or speech from one language to another.

For example- Google translate is a google cloud application that offers text translation into various languages. It uses Transalatron to develop the learning model. Neural Nets used are LSTMs and sequenced RNNs with an encoder-decoder model. 

Credit Card Anomaly Detection using Autoencoders 

The project aims at detecting fraudulent credit card transactions so the system can curb them and charge the customer of only the actual transactions. The dataset contains records of credit card transactions that are legal and fraudulent which have been passed through PCA (principal component analysis)analysis to change the data fields into numbers. We also have transaction amount, the time difference between consecutive transactions, and fraudulent transaction each unique credit card. Neural networks and autoencoders are used in conjunction with each other for modelling. And finally, the accuracy of the model is measured using Mean Squared Error (MSE) with the ggplot2 package. 

Credit Card Anomaly Detection Project with Guided Videos

6) Machine Learning Projects for Resume on Time Series Data

Time Series data helps predict an object’s behaviour compared to its older state in time. Time series is a dataset of continuous and periodic observations of the time instances attached to the data itself. Time Series finds use in many prediction scenarios like weather prediction, prediction for the price of an item, sales prediction, etc. It is much like prediction but with an added time constraint or feature, making it an altogether different problem of time-series forecasting. Generally more complex than traditional prediction projects.

Datatypes in Time Series

Time Series Data are observations recorded at different instances in time for a set period. 

Cross-Sectional Data

Data values of more than one variable are gathered at the same time. Thus, freezing or capturing the state of a system as a single entity in time. That is why the word cross-section comes into play, which implies a time-print of the model.

Pooled Data is the mixture of time-series data and cross-sectional data. 

Types of Time Series Modelling

Time series forecasting further divides into two subcategories based on the number of variables in the model, which are as follows:

Univariate Time Series Forecasting is when only one other forecasting variable is present in the model apart from time. For example, in a sales prediction model, the number of sales is the only one that will vary with time.

Multivariate Time Series Forecasting model is one where multiple variables are changing with time. Naturally so, the forecasting depends on these variables as well. For example, the temperature during the day depends on many variables like rainfall, wind, overcast etc. So essentially, a model to predict the temperature would be a candidate for multivariate time series forecasting .  

Overview of Seasonality and Autocorrelation in Time Series Data

Autocorrelation defines the similarity between a time series and its lagged version, showing the relationship between past and present values. Autocorrelation is also called lagged correlation or serial correlation. It ranges between the value of -1 to 1. 

Seasonality signifies periodic fluctuations in the graph of time series. Quite simply, it means that the data in the sequence repeats after a specific time called the period. Seasonality is generally calculated over one financial year.

Time Series Analysis and Forecasting Techniques 

ARIMA or Auto-Regressive Integrated Moving Average combines three models, i.e. ‘AR’, ‘MA’ and ‘I.’

AR shows the evolving variable of interest regressing over its initial values.  

MA shows that the regression error is the linear combination of error term values at previous instances. 

I shows that the data values are replaced by differences in their values from older values. 

Moving Average is so-called because each data point averages the data values before and after in the time series and creates a new time series. Moving Average highlights trends and trends cycle. It is ideal for univariate time series.

Exponential Smoothing creates the new time series by average the weight values from the current time series. A Datapoint in a time series has less weight if it's older in time compared to a recent data point.  The theory being that recent data has more chance of reoccurring again.

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Machine Learning Project Ideas for Resume on Time Series Data

Weather forecasting  .

Weather forecasting is a complex time series problem that uses past weather data and related parameters like wind pressure, overcast, wind speed etc., into account while forecasting future weather. 

The dataset contains the average temperature recorded in 2000 stations over Helsinki for some time. The SARIMA or Seasonal ARIMA model is used to model, and the Root Mean Squared value is used to check the accuracy.

Sales Prediction 

Sales prediction in a company is again a time series problem that considers the month of the year, holidays around and seasons in predicting future sales. The sales data show a cyclic trend in data that repeat every year. The dataset contains product information such as item id, item weight, type of the item, item MRP, etc. The dataset undergoes imputing of null values and one hot encoding. Outliers are identified with boxplot and deleted accordingly. Gradient boost tree and xgboost algorithms are applied for modelling, but the most efficient algorithm turns out to be a neural net with MLPRegressor. 

Project on Bigmart Sales Prediction with Guided Video tutorials

Stock Prediction 

Stock Market prediction depends on the historical stock records, geopolitical environment and company performance in recent times. It is a complicated prediction problem that involves time series along with deep learning.

The data is taken from the EU stock market with fields like the German DAX stock index, UK stock index, etc. We extract the trend and seasonality in the dataset and identify correlations and autocorrelations. Vector Autoregression (VAR) is used for modelling with good accuracy among other algorithms like ARIMA and LSTM.

Time Series Project on Stock Market with Source Code and Explanatory Videos

If you are a recent college graduate or in the final year of graduation, you know how difficult it is to create a data science or machine learning resume without prior work experience. However, adding diverse machine learning projects mentioned above can definitely add credibility to your resume.

It is essential to treat the various types of machine learning problems discussed above as a general guide since each project is unique and needs a precise approach. One can start by learning one project in each category and proceed from there. It is crucial to take note of learnings from each project and list them in the resume. 

Here’s a recommended list of blogs on different types of project ideas for further exploration and reading -

• 8 Newest Projects to Jump-Start the Data Science Journey
• Image Processing Projects
• Deep Learning Projects
• Data Science Projects
• 15 Data Mining Projects Ideas with Source Code for Beginners
• 20 Machine Learning Projects That Will Get You Hired in 2021
• 15 Data Visualization Projects for Beginners with Source Code
• 20 Web Scraping Projects Ideas for 2021
• 8 Healthcare Machine Learning Project Ideas for Practice in 2021
• Access Job Recommendation System Project with Source Code

1) How do you put machine learning projects on your resume?

Machine Learning projects should be brief and to the point on the resume. One can briefly discuss the dataset, model training, libraries used and accuracy by mentioning only the crucial points. 

2) Are Machine Learning projects good for a resume?

Indeed, machine learning projects are great additions to one’s resume. Machine learning is a burgeoning field and adding ML projects to the resume opens up job more opportunities. Candidates who wish to make a career in Machine Learning or Deep Learning need to build a versatile portfolio of ML projects for the resume.

3) Can one do Machine Learning projects in an Internship?

Yes, one can do machine learning projects in internships. In fact, during internships, one learns to build and deploy machine learning projects in real-time. It is an ideal environment to expand one’s experience and knowledge. But it is equally essential to be able to land an internship in the first place. It is best to start learning and practising machine learning projects at our own pace and slowly build an internship resume. By enlisting some prior understanding in machine learning projects, one can increase their chances of landing a machine learning internship. 

4) What projects can I do with Machine learning?

With Machine Learning, one can do many projects depending on the project type and theme.  A good strategy would be to pick one project from each category, as discussed above, for the resume. Face Recognition Project , Sales Prediction projects, Recommendation System Projects , Building a chatbot using NLTk , Spam mail detection project etc., are good choices to get started with gaining hands-on exposure to diverse kinds of problems.

5) How does one write a Data Science project for a resume? 

A data science project for a resume should have a brief introduction followed by a one-line explanation about the dataset and data-cleaning techniques involved. 

Following that, one should write about the models used and the model that produced maximum accuracy.  

It is crucial to remember not to be long-winded in describing the project and mention the significant points. 

In the end, you can conclude by remarking about the learnings obtained during the project and key takeaways.

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Top 10 Machine Learning Projects To Boost Your Resume

Daniel Bourke

Do you want to improve your Machine Learning skills, and create a kick ass project portfolio?

Well, good news!

Rather than bombard you with hundreds of random projects that cover the exact same principles, I’ve put together 10 ML projects (from Kaggle and other sources) that I think you should work on if you want to up your game, and have a stand out resume .

And better still? If you’re stuck for time and can only do a few of these, I’ve listed 3 projects on this list that you can’t miss, if you want to be ahead of the curve.

So let’s dive in…

⭐ Machine Learning Project 1: Dog breed classification

The first of my ‘can’t miss’ projects .

The goal is simple. You build a machine learning model to classify 120 different dog breeds and then you can even take it and try it on your own dog. (Because who doesn’t love dogs!?).

This project is a fun way to get started with computer vision models (aka convolutional neural networks or CNN’s), and teaches you multiple skills, such as:

• Downloading a dataset
• Building a CNN
• Using transfer learning (for better results)
• Testing the model on real world images of dogs

You can check out the project here .

Sidenote: This is such a valuable project to build and learn from, that I actually use this as a follow along project inside of my Machine Learning and Data Science Bootcamp course !

You can even check out a preview of the course here for free .

⭐ Machine Learning Project 2: Deploy your own machine learning model with Gradio

The 2nd of my ‘can’t miss projects’ to complete .

Deploying a model is just as important as training a model, because by deploying a model you're making it available to people other than yourself .

This means they can also try it and see where it has errors or goes wrong, which helps you to further improve the model later on.

Crowdsourcing improvements for the win!

To run this project, you’ll be using a tool called Gradio :

Gradio is a Python framework for creating machine learning interfaces where people can upload their own data and try out your model.

You can grab the project code here , and I’ll walk you through the process.

⭐ Machine Learning Project 3: Create a podcast transcriber with OpenAI’s Whisper

The 3rd and final of my ‘can’t miss’ projects .

OpenAI has a great transcription model called Whisper .

There’s a lot of podcasts out there, and many of them don’t have transcriptions. This sucks, because having a podcast in text-form enables someone to search the text and jump around in the audio to find the most relevant parts to them.

This project will help you learn how to:

• Solve a problem with custom data, and
• Build something that could actually be useful for you and others (and maybe even impress your friends)!

If you have a favorite podcast, you could even make a Python app that transcribes the podcast for you and turns the audio into searchable text.

Editor’s note: This is a great way to get on the radar of particular influencers, if you want to build relationships for mentoring, advice, help getting hired, etc. Transcribe a few episodes, reach out, and boom! New best friend.

You can check out the course and project here .

So those are the top 3 projects that I recommend to help you get a broad understanding and practice with Machine Learning. If you work on those projects alone, you’ll pick up some vital skills, while also having some decent portfolio work to add to your resume and impress prospective employers. However, if you want to go a little deeper and really make your resume stand out, then here are a few extra projects that you can try...

Machine Learning Project 4: Predict which passengers might survive the Titanic sinking

Now, this might seem like a pretty grim project, but it's actually something you might do if you’re building models for insurance companies or similar type industries.

Why? Well, insurance companies base their premiums (how much they make you pay) mainly on:

• Specific demographic factors and
• Predictions of the likelihood of events happening to the prospective customer

Negative event = insurance company will have to pay some money to you. This means they don't make a profit. They don't want this 😉.

Of course, the likelihood of a negative event (ex: injury or death) will be dependent on the type of activities someone will be doing (ex: driving a car vs. driving a motorcycle or relaxing at an all-inclusive resort vs. skydiving).

So these machine learning models can be used to run different scenarios and make these predictions more accurate. The more accurate the prediction, the more competitive the insurance company can be with pricing their premiums which means they make more money!

As you can guess, being able to create prediction models that fit this is a valuable skill for companies. So this makes it a perfect project to put on your resume and to talk about in your interviews .

In this project, you’ll be running a model on the 1912 Titanic disaster. If you don’t know the history, during its maiden voyage, the British passenger liner RMS Titanic sank after colliding with an iceberg in the North Atlantic Ocean.

Possibly due to negligence or poor planning, there were not enough lifeboats on the ship to cover each person, which resulted in the death of 1,502 out of 2,224 passengers present on the ship 🙁.

This machine learning project focuses on building a model that can predict the survival probability of passengers on the Titanic, based on factors like age, name, economic class, etc.

This project helps you in getting a good understanding of classification problems. You can find the sample dataset from our friends over at Kaggle here .

Machine Learning Project 5: House price prediction with regression analysis

Linear regression is a core aspect of machine learning algorithms, and every machine learning engineer or enthusiast is expected to have a thorough understanding of how to use it.

However, if you’ve not come across this topic before, linear regression is applied to predict continuous variables from a set of features .

Why care and what does that mean? Well, by understanding how to use linear regression, it can help us to resolve a range of ML problems, such as predicting housing prices.

This project will help you understand ML basics such as data manipulation.

You can get the sample dataset to use here .

Machine Learning Project 6: Handwriting recognition

MNIST (otherwise known as the Modified National Institute of Standards and Technology), is the most basic dataset for computer vision.

It was introduced in the year 1999 and has since served as the foundation for benchmarking classification algorithms.

Similar to the first doggo project from earlier in this list, but a little simpler. In this project, you are going to train a Machine Learning algorithm to accurately recognize handwritten images of digits ranging from 0 to 9.

ML skills you’ll learn include:

• Importing and cleaning data
• Preprocessing data
• Applying different ML algorithms to data collection
• Determining the best metrics to gauge algorithm performance, etc

Check the sample dataset here .

Machine Learning Project 7: Music genre classification

Audio has proven to be fairly difficult for ML algorithms to learn. However, we can help it get better!

By categorizing music based on how it sounds, we can develop a model for identifying audio recordings into different musical genres, such as pop, rock, romance, and so forth.

Get the sample dataset here .

Machine Learning Project 8: Moneyball

Data science and statistical analysis became widely known in the general public, partly because of the story behind the ‘Moneyball’ movie.

Spoilers for a 12 year old film, but it's the tale of how an underdog baseball team with very little money or success, became the league champions, as well as achieving a record breaking 20 consecutive wins!

How did they achieve this? Sweet, sexy, statistics baby! Instead of trying to compete by purchasing the most expensive players who hit sensational home runs, they instead performed statistical analysis to find the consistent performers amongst the cheaper players.

The stats were calculated using parameters such as on-base percentage (OBP) and slugging percentage (SLG), which are very important when scoring runs but are often undervalued by most scouts and old school baseball executives.

This way, they hoped to build a team that would win, based on the math in their model vs. outlier conditions. More runs per game = more wins over time.

The goal of this project is to help you build a similar winning team. You’ll be using machine learning to extract insights from historical data for baseball, football, and basketball.

You sports fans out there will love this one. Maybe this can be your first step to working for a sports team!

Grab the sample dataset here .

Machine Learning Project 9: Predictive data analytics for Supermarket sales

Kind of similar to the housing prices project from earlier, but applied slightly differently.

The goal of this project is to develop a regression model to forecast sales of each product at a supermarket in the upcoming year. This will then help in identifying sales trends and implementing practical business tactics to generate revenue - a key skill to have for almost every e-commerce or physical product company.

(Otherwise you might not order enough bananas… 😱)

You can check the sample dataset here .

Machine Learning Project 10: Credit card fraud detection

Credit card fraud cost over an estimated $30 billion dollars in the US alone back in 2021 , so as you can guess, being able to detect this and stop it from happening is fairly important.

This project aims to build a fraud detection model for credit card fraud, by looking for systemic anomalies to avoid scams and unauthenticated transactions.

You can check out the sample dataset here .

So what are you waiting for? Start practicing and building these Machine Learning projects today!

So there you have it. My top 10 recommend beginner to advanced Machine Learning projects for you to work on, up your game, and make your resume pop.

Remember though, if you’re stuck for time then I recommend you work on the Top 3 projects first as these can have some of the biggest benefits for your skill development, while also covering a lot of what you need to know and practice.

If you’re just starting out or find yourself stuck on any of these projects, then be sure to check out my Complete Machine Learning and Data Science Bootcamp .

• It’s one of the most popular, up-to-date, and highly recommended ML courses online
• Just like every course here at ZTM, it's been fully updated for this year (and will continue to be updated every year)
• You learn by actually building and coding along with me
• It's designed to take you from complete beginner to learning the latest version of Python, Tensorflow 2.0, and all the latest industry skills and trends to the point that you can confidently build your own projects and actually get hired this year

The best part? You get to learn alonside 1,000s of fellow students in our private Discord community , as well as direct access to me, so you can ask any questions and learn faster and easier than ever before!

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5 Attractive Machine Learning Projects For Resume (2022)

“Machine learning is something every developer at every company needs to know about, right now." - Kevin Scott, CTO, Microsoft.

Artificial Intelligence (AI), Machine Learning (ML), and Data Science are the hottest fields in technology right now. From healthcare and agriculture to manufacturing, energy, and retail, many companies across industries are leveraging these technologies to get ahead.

Machine learning is popular because there is an abundance of data to learn from today and luckily computation is abundant and cheap today.

Machine learning is the study of making decisions under uncertainty: given a training dataset, how should I act when I see something new - and trust me this technology is the new black.

But it is truly intimidating to explore these domains, especially if you are a newbie. The reason being, there is no foolproof roadmap to master AI or be a skilled Data Scientist, the skills and tools needed are dynamic.

Don’t miss!

Roadmap to become a backend or full-stack developer:

Demands of every company for their Machine Learning problems are different, and it is your duty as an AI/ML engineer or Data Scientist to quickly adapt and deliver them a solution. For this you need to have rock-solid experience in using Machine Learning technologies and Data Science tools.

Now what is the best way to master new skills? Follow tutorials? Umm yes, but only for a while. To master the skills you need to implement projects on your own .

Crio Projects Hub offers you some of the latest and trendiest Machine Learning projects that you can implement all by yourself!

So, if you are in search of the most in-demand and most-exciting career domains, gearing yourself up with Machine Learning skills together with Crio Projects Hub is a kickass move now.

Save for later: List of 20+ projects in Python, Machine Learning, Java, more

Top 5 machine learning projects, 1. visualizing and forecasting stocks using dash.

You must have seen various stock charts in movies and tv shows. The traders at the Stock Exchange or Wall Street actively study, analyze, and keep track of the stock prices of various companies’ shares. These charts make it easier for traders to visualize the behavior of the stocks and also predict trends accordingly. But what if you could take these numbers one step ahead and predict the future stock price for a specific date?

In this project, you will be creating a single-page web application using Dash (a Python framework) and some Machine Learning models which will show company information (logo, registered name, and description), and some stock plots based on the stock code given by the user. Also, the ML model will enable the user to get predicted stock prices for the date inputted by the user. Wrapping it, you will be deploying your app using Heroku.

Exciting stuff? Isn’t it. Become the next wolf of wall street using this amazing tool ;)

Download interesting mini projects to get hands-on with Python programming, Data visualization, Selenium, APIs, and more

Skills to hone.

By end of the project , apart from learning to write clean modular code using Python you will also learn some major skills and tools such as:

• Dash - Dash by Plotly is extremely efficient in building data visualization apps with a highly customized user interface (interactive web apps) without having to learn JavaScript or frontend web development. It is an ideal tool for Python web developers.
• Data Visualization - Data visualization careers tend to come with high salaries. Often, these specialists can work from home, and they also benefit from opportunities available across many different industries. In general, employers look for data visualization professionals who regularly increase their skills and knowledge in the field.

This Python-based Machine Learning project will help you kick start your career in Data Science, specifically data visualization.

Simple (15-hour) projects to strengthen your Machine Learning - Data Visualization skills:

Also, this is an amazing project for web development using Python technologies.

This project is also a good exposure to Machine Learning and Artificial Intelligence enthusiasts as this project briefly explore one of the sub-domains of AI, namely predictions using Machine Learning models.

Learning relevant skills of this project will be additional support and a star point when you collaborate with a different tech stack to build similar visualization models especially in the markets domain. Mastering a higher level of these skills will yield a very promising career in the field of Artificial intelligence or Data Science.

So go ahead upgrade your portfolio with this amazing project up your sleeve!

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Who should try this.

• Prerequisite knowledge required

Basic knowledge of Python concepts, basic HTML, and CSS will be required to execute this project. All additional skills can be learned through the course of implementing this project.

An easy project to strengthen your Python, HTML, CSS skills:

• Difficulty level to expect

Of all the Machine Learning projects for beginners, this is a must-try for Python web developers.

It is also a good project for intermediate developers and a refresher project for Python (web) development professionals.

• Time needed

25-30 hours is the maximum expected time needed to complete this project.

2. Machine Learning Preprocessing CLI

Before applying Machine Learning on any dataset, you need to convert it in such a way that the algorithms can understand the dataset. These steps are preprocessing steps.

Data preprocessing is an integral step in Machine Learning as the quality of data and the useful information that can be derived from it directly affects the ability of your model to learn.

Data is said to be unclean if it is missing attribute, attribute values, contains noise, or outliers, and duplicate or wrong data.

Therefore, it is imperative that you preprocess your data before feeding it into your model. But data preprocessing is often considered time consuming and tedious by many Machine Learning developers.

And this project changes that for all developers.

In this project, you will be building a simple CLI tool that will save time by performing the data preprocessing in a faster, and convenient manner.

Beginner level project to sharpen your basics in Data Visualization & Data Preprocessing:

By end of the project , you would have conquered the following concepts:

• OOPS - Python, like others is an object-oriented language. An object-oriented paradigm is to design the program using classes and objects. Using classes and objects while writing python scripts is very common and OOPs concepts helps in this process.
• Pandas - Pandas are really powerful. They provide you with a huge set of important commands and features which are used to easily analyze your data. We can use Pandas to perform various tasks like filtering your data according to certain conditions, or segmenting and segregating the data according to preference, etc. It is one of the most widely used python libraries in the AI regime today.
• Exception handling - It is a very popular technique for incorporating fault tolerance into software systems. It allows developers to structure the redundant code that is added to deal with the exceptional conditions that may occur, separating it from the code responsible for the normal operating flow.

Another awesome project to develop your skills in Python programming, Pandas, and also learn Plot.ly

This is a good learning project for all Python developers, especially those in the Machine Learning field, as they can easily relate to this tool’s importance. Not just that, it is a stellar project to boost your résumé!

Also read: Software Developer Resume Tips - The Ultimate Resume Guide

Basic knowledge of Python OOPs concepts will be required to execute this project. All additional skills can be learned through the course of implementing this project.

It is a perfect Machine Learning project for beginners. Intermediate Python developers will also walk away with valuable skills by the end of this project.

30-35 hours is the maximum expected time needed to complete this project.

3. Exploratory Analysis of Geolocational Data

Want to experience a day in the life of a Data Scientist/Data Engineer? This project will take you through a full-fledged analysis of real-life data - from data preparation on real-life datasets, to visualizing the data and running Machine Learning algorithms, to presenting the results.

In this project, you will fetch, clean, analyze, and run k means clustering on geolocational data to recommend the best accommodations in a city!

By end of the project , you would have a deeper understanding of analysing real data and gained practical knowledge of the following skills:

• K means clustering - K means clustering is one of the most popular clustering algorithms and usually the first thing practitioners apply when solving clustering tasks to get an idea of the structure of the dataset. The goal of k-means is to group data points into distinct non-overlapping subgroups.
• Data visualization - Data visualization careers tend to come with high salaries. Often, these specialists can work from home, and they also benefit from opportunities available across many different industries. In general, employers look for data visualization professionals who regularly increase their skills and knowledge in the field.
• API - It is a service for developers. Every time developers write a new program, they don't reinvent the wheel for doing some core application. Instead, they can contract out certain responsibilities by using already created APIs that do the job better.

You will be using some amazing Python libraries such as Pandas, Matplotlib, ScikitLearn, Folium, to execute the tasks involved in this project.

Not just that, you will also be using the FourSquare API; for what? Find out while doing the project ;)

Download now: 20+ mini projects explained step-by-step for beginners and advanced developers

A basic knowledge of Python concepts, Machine Learning models will be required to execute this project. All additional skills can be learned through the course of implementing this project.

This beginner Python project is perfect for those looking to sharpen skills in Machine Learning/Artificial Intelligence. This project is packed with learning takeaways even for for intermediate Python developers and Machine Learning professionals.

15-20 hours is the maximum expected time needed to complete this project.

4. Chatbot Song Recommender System

Do you want to see the power of AI without doing any actual Machine Learning/Deep Learning? If yes, then you are surely going to love this project.

In this project, you will be building your own version of a chatbot ( Cakechat Chatbot ) that recommends songs based on the emotional analysis of the conversation.

This song recommendation feature employs the use of Last.fm API, very much similar to the popular Spotify API. Also for tone/emotion analysis of the conversation you will use the IBM Tone Analyzer API.

Collaborating with these types of APIs adds significant value to your skills as the popular chatbots these days do much more than having a simple data-driven conversation.

Also the reason to choose Python to build the chatbot is because Python boasts a wide array of open-source libraries for chatbots, including scikit-learn and TensorFlow. It is great for small data sets. Not to forget, Python's libraries are much more practical.

By end of the project , you would have gained solid experience of using the following:

• Chatbots - Learning to build chatbots is very useful for developers. This is so because building a chatbot involves the usage of a lot of tools/skills. These range from direct usage of APIs or some cognitive services to developing your own AI models and tailoring the bot as per your needs. A more sophisticated version of chatbots can be virtual assistants like Siri, Alexa. Knowing to build such chatbots is very much desirable in the software industry today.

You will get hands-on experience with various cognitive services, amazing tools, all of which you will be combining and wrapping using Python to build this amazing song recommending chatbot.

Every company, be it airlines, health, food, etc., seeks developers who have a deep understanding of building such chatbots. This is a project that recruiters will be impressed to see in your portfolio!

Add few more interesting bots to your project experience:

Get offline access to real-world mini projects that will stand out in your resume

Basic knowledge of Python concepts (knowing some libraries too will be a plus) will be required to execute this project. All additional skills can be learned through the course of implementing this project.

It is a perfect Python project for beginners, focusing on AI. This is a great project for intermediate Python developers and ML/AI professionals who are looking to showcase sought-after strengths with an interesting project. It is also a great pick for those who are looking for machine learning projects for final year project.

50 hours is the maximum expected time needed to complete this project.

5. Companion App: A mental health tracker built using Flutter and Firebase

Mental health is an important issue in the world today. This simple Flutter app is a small step towards finding a universal solution for all.

In this project, you will build a friendly mental health tracker that helps its users get through their problems in an interesting way. With personalized tasks and timely progress checks, this companion app is a great solution for those who are suffering from mental health problems.

This app primarily being a Flutter-based app development project still has a great potential of using advanced Machine Learning models to have much more sophisticated features.

Jump right into the project to understand how Machine Learning has been leveraged here to successfully build this practical app .

By end of the project , you will not only master the art of writing clean modular code using Dart, but also gain hands-on experience with these noteworthy skills:

• UI/UX - UX/UI design of the application improves the user experience and customer satisfaction that ultimately helps increase the number of users of the specific application. The UI and UX design helps to win the consumers' confidence and compels them to use your application with solutions to what they are looking for.
• Flutter - Since Flutter was introduced, it has rapidly shown massive growth. Its demand is increasing exponentially in the market and all the businesses want to hire Flutter app developers for their app development and maintenance.
• Cloud Firestore - It is Firebase's fully managed cloud -native NoSQL document database that is fast and serverless. It simplifies syncing, storing, and querying data for mobile, web, as well as IoT apps. With the benefit of Google Cloud , it offers great scalability. It provides live synchronization and offline support.
• Classification - It is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks.

This project is an amazing example of app development using Flutter and also integrating Machine Learning models to make it a practical app.

Advance your app development skills (using Flutter) by building a Hyperlocal Ecommerce platform:

Basic knowledge of Dart (and preferably Flutter too) will be required to execute this project. All additional skills can be learned through the course of implementing this project.

This is a challenging project for beginners in Flutter. Intermediate developers with basic knowledge of Dart and Flutter will have fun building this app whilst learning new things about accessibility, design practices, and fine-tuning the app for the target audience. This is a great project for advanced web developers to showcase app development skills.

60-65 hours is the maximum expected time needed to complete this project.

These ML projects have been contributed to Crio Projects Hub by Crio's budding community of developers. If you found these ideas interesting, show your love by liking this article. We would also love to know the project that got you excited the most - let us know in the comments below.

If you are wondering how to contribute your project to the hub, head to Crio Projects Hub and submit your project idea.

Before you go..

Don’t miss the comprehensive list of 20+ mini projects that cover all the essential skills you need to make your resume stand out like:

• Bot building
• Serialization-Deserialization
• Android basics
• Game Development

And more...

Written by Kevin Paulose

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Machine Learning Resume : Sample and Writing guide

Creating your machine learning resume.

In a data-driven era where insights hold the key to unlocking innovation, machine learning has emerged as a game-changer. Imagine a world where algorithms learn from data, uncover patterns, and make intelligent predictions. As you venture into the realm of machine learning, your resume becomes the gateway to landing your dream job in this rapidly evolving field. In this blog, we will guide you through the art of crafting a compelling machine-learning resume, equipped with industry insights, sample resumes, and key factors that will help you stand out in the competitive landscape of this transformative discipline.

• Follow the right format
• Clearly classify Education Section
• Add relevant Skills clearly
• List relevant experience in Machine Learning  
• Add other sections to standout
• Fonts – Professional resume fonts such as Calibri and Didot
• Font size – Use 13-14 for headings and 11-12 for other texts
• Format – The Reverse chronological format is the most recommended
• Line Spacing – 1.1.5 ( recommended )
• File Type – PDF’s are the most preferred file type
• Use clutter-free design and do not fill every part of your resume with text
• Using bullet points wherever possible is a better practice as compared to paragraphs
• Always use active voice
• Use simple vocabulary and shorter sentences
• Do not try to fit in everything on one page. Use more pages if you feel the need for it but keep the number of pages as limited as possible.
• Edit until you get a draft which is concise, clear in understanding, looks good visually, and includes all that you want to tell the recruiter about you
• Use online tools such as Grammarly to self-check your draft
• Have it proof-read by a third party, preferably a friend or a daily member who would give you genuine advice
• A Bachelor’s degree in either computer science or in a related field
• A good amount of prior experience with GPU computing and data mining
• A general background in NLP and deep learning, along with their corresponding tools and techniques
• Basic experience with agile software development practices

Character traits

• Analytical and critical thinkers 
• Data-driven performers
• Clear communicators to translate and understand complex information
• Problem solvers and innovator

Machine Learning Engineer Skills

Hard skills.

• Data Modeling
• Predictive Modeling
• Classification
• Clustering Models
• SciKit Learn
• Unit Testing and CI/CD
• Machine Learning technology
• Explanatory Analysis
• Natural Language Processing
• Strong Programming Skills 
• Data Structures

Soft Skills

• Time Management
• Critical Thinking
• Organizational Skills
• Interpersonal Skills
• Presentation Skills
• Teamwork and Collaboration
• Written and Verbal Communication
• Problem Solving
• Attention to Detail

Probability and Statistics

The theories of probability are the mainstays of the most machine learning algorithm. Being familiar with probability enables you to deal with the uncertainty of data. Getting a grasp of the probability theories like  Python , Gaussian Mixture Models, and Hidden Markov Models; is a must if you want to be considered for a machine learning job that centres around model building and evaluation.

Closely linked to probability is statistics. It provides the measures, distribution and analysis methods required for building and validating models. It also provides the tools and techniques for the creation of models and hypothesis testing. Together, they make the framework of the ML model building. This is the first thing to consider when building your machine learning resume.

Computer Science and Data Structures

Machine learning works with huge data sets, so fundamental knowledge of computer science and the underlying architecture is compulsory. Expertise in working with big data analytics , and complex data structures, are a must. Thus, a degree or a formal course in these domains is required for a machine learning career . Your resume must display your skills at working with parallel/distributed architecture, data structure like trees and graphs, and complex computations. These are required to apply or implement, at the time of programming. Additional certifications for practising problems and coding will hone your ability with big data and distributed computing. Experience in computer science applications will go a long way in securing you a job in this field.

Read Also: Linear Regression for Beginners – Machine Learning

Programming Languages – R, Python, Java

To apply for a job in Machine learning, you are required to learn some of the commonly used programming languages. It implements any language with the essential components and features, even though it is largely bound by concept and theory. Some programming languages are considered especially suited to complex machine learning projects. So, working knowledge of these programming languages adds value to your machine learning resume.

Using C/C++ when memory and speed are critical, helps to speed up the code. Many ML libraries are also developed in C/C++ as they are suited for embedded systems. Java, R & Python work very well with statistics. Python has several machine learning-specific libraries that make use of efficient processing, despite being a general programming language. Knowledge of Python helps train algorithms in various computing architecture. R is an easy-to-learn statistical platform, it’s use in ML and data mining tasks is increasing.

If you are a fresher or an Entry – Level professional, give detailed information about the projects that you have done.

Pro Tip : Do not shy away from giving all possible details about work experience and achievements, flaunt what you have achieved.  

An application for machine learning job role requires careful planning and consideration. Machine learning is all about algorithms, which in turn stems from a good knowledge of big data analytics and requisite programming languages. Sound engineering or technical background is a must. By including these skills in your machine learning resume, you are increasing your chances of being selected. So, are you all set for a career in machine learning?

Take up the Basics of Machine Learning Free Online Course by Great Learning Academy and learn the basic concepts required for you to kick-start your machine learning journey. Great Learning offers various Artificial Intelligence Courses that you can choose from. Upskill with the help of mentored learning and dedicated career support.

Other Sections

A  degree, certificate or online diploma in these languages , ensures a good resume. As an engineer or student of science, you may already be skilled in C++, Java, and Python. You can also learn these languages online in your spare time, and practice on projects for special mentions on your CV. Programming languages like Python and R make it easy to work with data and models. Therefore, it is reasonable to expect a data scientist or machine learning engineer to attain a high level of programming proficiency and understand the basics of system design. Read Also: 100 Most Common Machine Learning Interview Questions

Machine Learning Algorithms: Applying machine learning libraries and algorithms is part of any ML job. If you have mastered the languages, then you will be able to implement the inbuilt libraries created by other developers for open use. For instance, TensorFlow , CNTK or Apache Spark’s MLib , are good places to work upon. You can also begin with practising programming algorithms on Kaggle. You can mention this in your ML resume as well. 

Software Engineering and Design: Software Engineering and System Design, are typical requirements for an ML job. A good system design works seamlessly, allowing your algorithms to scale up with increasing data. Software engineering practices are a necessary skill on your resume. As an ML engineer, you create algorithms and software components that interface well with APIs. So technical expertise in software designing is a must while applying for a machine learning job.

Machine Learning Fresher Resume

When it comes to freshers, of course, they have no experience to showcase. Here, you focus more on your projects, certifications, internships, technical skillset, and soft skills .

The important skills to showcase on a resume are:

• Programming skills
• Data modelling and evaluation
• Machine Learning algorithms and libraries that you have worked with

The soft skills are the ones that make you an ideal employee and help the company function better. You can mention select accomplishments that showcase these skills, such as:

• A time you were a valued team member
• A time where you lead a team
• The specific problem you identified and solved
• When you followed directions
• A scenario where you stepped up beyond your responsibility

Explicitly explain the following points in your resume:

• Machine Learning Projects with objective, approach and results.
• Knowledge of any programming language
• Proven expertise in solving logical problems using data
• Training or internship in data analytics or data mining
• Highlight if you know Python or R

Your resume should be structured like this:

• Resume heading
• Personal and contact details
• Career objective
• Certifications
• Internships
• Technical Skills and Soft Skills

Pro Tip: Always edit and restructure your resume on the basis of the job you are applying for. Accommodate job requirements in your skillset, achievement sections.

• Showcase Machine Learning Expertise: Highlight your proficiency in machine learning algorithms, frameworks, and libraries such as TensorFlow, scikit-learn, and Keras. Demonstrate your understanding of various techniques such as regression, classification, clustering, and deep learning.
• Emphasize Data Manipulation Skills: Showcase your ability to preprocess, clean, and transform data using tools like Pandas, NumPy, and SQL. Highlight your expertise in feature engineering and selection to improve model performance.
• Highlight Domain Knowledge: If you have experience working in specific industries or domains, showcase your understanding of industry-specific challenges and the impact of machine learning in solving them. Demonstrate your ability to translate business problems into machine-learning solutions.
• Demonstrate Communication and Collaboration Skills: Showcase your experience working in cross-functional teams, collaborating with stakeholders, and effectively communicating complex ideas to both technical and non-technical audiences. Highlight your ability to present data-driven insights and make recommendations for informed decision-making.

General tips to keep in mind :

• There is no need to downplay your achievements and success. If there’s a place to boldly talk about your accomplishments, it’s on your resume.  
• There is no need to fill every inch of your resume with text. White spaces provide a cleaner look to the document, making it much easier for the reader to comprehend. A good idea will be to adapt existing templates online, that equate well to your preferences.
• Ensure that the writing is concise and to the point; eliminate any extra verbiage, unless necessary.
• Do not confine your resume to a single page, there is no one-page mandate. As long as there exists relevant experience, the extra room is justified. 
• Have it proofread, either online (on tools like Grammarly) or by a family member. This is useful to spot unseen errors and provide an outside perspective.

Further Reading

• Machine Learning Interview Questions and Answer for 2020 You Must Prepare
• 100+ Data Science Interview Questions for 2020
• Python Interview Questions and Answers for 2020
• NLP Interview Questions and Answers most commonly asked in 2020
• Python Developer Resume Samples| How to Make Python Resume?
• Top 20 Artificial Intelligence Interview Questions for 2020 | AI Interview Questions

Find Machine Learning Course in Top Indian Cities

Top Free Courses

Advantages and Disadvantages of Artificial Intelligence

Generative Artificial Intelligence Implications for Industry Experts

Cómo la Ciencia de Datos y el Machine Learning están reinventando la Planeación de Eventos

Unveiling the Potential of Artificial Intelligence Markup Language

Navigating the Deepfake Revolution: Implications for Gen AI, Bots, and More

Navigating the Future: The Rise of App-less AI Smartphones

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Table of contents

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• Resume Examples
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5 Machine Learning Resume Examples - Here's What Works In 2024

Machine learning engineers program computers to perform advanced predictions. this is a sub-field of artificial intelligence in which computers can learn from experience without human intervention. to become a machine learning engineer, you should ideally have advanced training in data science or computer science. if you are passionate about data science and would like to land a job as an ml engineer, read on. we’ll help you craft your machine learning engineer resume with our expert advice and templates..

Machine learning engineering is part of artificial intelligence’s sub-branches. It consists of developing high-performing predictive systems and applications through data modeling and algorithms. These programs work with large volumes of data and learn from it to deliver more accurate predictions. 

Machine learning engineers are responsible for delivering these applications and systems. They must also perform machine learning tests to measure the efficacy of statistical analysis. 

Machine learning is a highly in-demand field used for different purposes, including credit scoring, facial recognition, and brain tumor detection. Almost every industry can benefit from the magic behind machine learning engineering. 

Hence, it’s no surprise to see that the demand for machine learning engineers is expected to rise by up to 22 percent, according to the Bureau of Labor Statistics. This is considerably high compared to the average demand outlook for most occupations. Yet, this is no excuse to neglect the quality of your resume. 

Despite the high demand for machine learning engineers, you should still give your potential employer reasons to hire you. Today, we’ll explore some industry-relevant techniques to improve the quality of your resume and increase your odds of getting a job as a machine learning engineer. Don’t forget to check our four resume templates.

Machine Learning Resume Templates

Jump to a template:

• Machine Learning Engineer
• Machine Learning Intern
• Senior Machine Learning Engineer
• NLP (Natural Language Processing) Engineer
• Artificial Intelligence Specialist

Jump to a resource:

• Keywords for Machine Learning Resumes

Machine Learning Resume Tips

• Action Verbs to Use
• Related Data & Analytics Resumes

Get advice on each section of your resume:

Template 1 of 5: Machine Learning Engineer Resume Example

A machine learning engineer creates autonomous systems that use large volumes of data to deliver accurate predictions. These applications learn from experience and need minimal to zero human intervention. Additionally, you should select the appropriate data visualization techniques and tools. This is a skill you might want to include in your resume.

We're just getting the template ready for you, just a second left.

Tips to help you write your Machine Learning Engineer resume in 2024

   indicate your ability to work with cloud technology..

Cloud technology has become more popular over the last decade due to its security and accessibility. This makes it essential for machine learning engineers to use them, so most employers might expect you to be able to use tools like AWS or Microsoft Azure. If you are familiar with cloud technology, indicate it in your resume.

   Include industry-relevant data science or machine learning certifications.

A great way to set you apart from your peers is by including relevant certifications on your resume. This way, you are showing evidence of your skills and level of experience by a validated source. Some of the best certifications you can use are AWS Certified Machine Learning and Google Cloud’s Professional ML Engineer Certification.

Skills you can include on your Machine Learning Engineer resume

Template 2 of 5: machine learning intern resume example.

A machine learning intern assists the development team by writing code to develop autonomous systems. You should ideally have mathematical skills, basic programming and data science knowledge, and a passion for AI. If you are struggling to include something in your work history section, you can also include volunteering experience on your resume.

Tips to help you write your Machine Learning Intern resume in 2024

   mention the programming languages and frameworks you are familiar with..

Your skills section should be highly technical, so it’s important to fill it in with hard skills like the programming languages and tools in your toolkit. This will give recruiters a better idea of the type of ML systems you can build. Some of the most popular coding languages in ML are Python, Java, and C++.

   Include machine learning projects you developed.

It’s ok if you don’t have extensive experience in the field. In the end, an internship experience will give you the expertise you need. You can include independent ML projects on your resume to help you demonstrate your potential.

Skills you can include on your Machine Learning Intern resume

Template 3 of 5: senior machine learning engineer resume example.

A senior machine learning engineer is an experienced professional who develops machine learning models, conducts research, and supervises less experienced colleagues. It is your responsibility to ensure that ML projects run smoothly. This is a role that requires seniority, so it’s important to highlight your education and relevant work experience on your resume.

Tips to help you write your Senior Machine Learning Engineer resume in 2024

   indicate your ability to work with deep learning..

Deep learning consists of neural networks with multiple layers. It is an ML technique that resembles the human brain’s functionality. Hence, you can work with large volumes of data to create high-performing systems. As a senior machine learning engineer, you should be familiar with this technique.

   Demonstrate your ability to supervise under-experienced colleagues.

Senior machine learning engineers often supervised other colleagues with less experience. They are also part of onboarding and occasionally participate in the hiring process. You should demonstrate this in your resume.

Skills you can include on your Senior Machine Learning Engineer resume

Template 4 of 5: nlp (natural language processing) engineer resume example.

Natural Language Processing is the process of developing devices and applications that understand human speech and sounds. These programs analyze the structure of speech to determine the meaning of words. Today, this is a highly in-demand field of artificial intelligence, and we use it unnoticeably in our daily life, such as voice recognition keyboards and remote controls. You should indicate your syntactic and semantic parsing skills on your resume.

Tips to help you write your NLP (Natural Language Processing) Engineer resume in 2024

   highlight your ability to write clean code..

When we’re working in a solo team, we often underestimate the value of writing clean code because it’s just us, and we understand our methods. However, this changes when working in a technical team where you need to collaborate with colleagues. Writing clean code is particularly important for this occupation due to the complexity of the field. Your peers must be able to understand your work in order to have seamless results.

   Showcase your statistical analysis skills.

Eventually, all that data you collect and process has a purpose: helping organizations make more informed decisions and improve their services. That’s why it is important to have statistical analysis skills and showcase them on your resume.

Skills you can include on your NLP (Natural Language Processing) Engineer resume

Template 5 of 5: artificial intelligence specialist resume example.

As an Artificial Intelligence (AI) Specialist, your role is as fascinating as it is complex. You live in a world where you're programming machines to mimic human intelligence. That's no small feat. Companies are increasingly relying on AI to solve problems, make predictions and drive improvements, an evolution that's ramped up the demand for AI specialists. Given this trend, your resume needs to show that you're not just familiar with AI, but you're a master of it. Furthermore, as AI continues to evolve and get more sophisticated, companies are searching for candidates who can keep up with shifts in the field. So, when crafting your resume, don't just focus on what you've done, but how you're continually learning and staying ahead of the game.

Tips to help you write your Artificial Intelligence Specialist resume in 2024

   emphasize your proficiency in ai-related programming languages and tools.

As an AI Specialist, you're expected to be proficient in languages like Python, R, and Java, and be comfortable working with AI-related tools like TensorFlow or Keras. Hence, don't just list these, but provide examples of how you've applied them to build or enhance AI systems.

   Showcase your understanding of AI trends and ethics

An understanding of trends influencing the development and application of AI is crucial. So, describe any ongoing learning or certifications you're pursuing. Additionally, articulate your understanding and consideration of AI ethics. Include instances where you've had to consider the ethical implications of AI systems you've worked on.

Skills you can include on your Artificial Intelligence Specialist resume

We spoke with hiring managers and recruiters at top companies like Google, Amazon, and Microsoft to gather their best tips for creating a strong machine learning resume. They emphasized the importance of showcasing your technical skills, relevant projects, and business impact. Use the following tips to make your machine learning resume stand out from the competition and land your dream job.

   Highlight your machine learning skills

Hiring managers want to quickly see if you have the right technical skills for the job. Make sure to list your proficiency in key machine learning skills, such as:

• Python, R, Java, or C++
• TensorFlow, Keras, PyTorch
• Scikit-learn, Pandas, NumPy
• Data visualization tools like Matplotlib or Tableau

Don't just list the skills though. Provide concrete examples of how you used them to drive results:

• Built and trained deep learning model using TensorFlow to classify images, achieving 95% accuracy
• Analyzed 10 GB dataset with Pandas and scikit-learn to identify top customer segments

   Show the impact of your machine learning projects

The best machine learning resumes go beyond just listing skills and duties. They show the real-world impact and value you delivered through your work.

Compare these two examples from a machine learning resume:

• Built a recommender model for e-commerce site
• Created churn prediction system

Instead, quantify your achievements like this:

• Developed content recommender system that increased average order value by 20% and customer engagement by 15%
• Built machine learning model to predict customer churn, resulting in $2M in retained revenue

Use numbers, percentages, and dollars to communicate your impact and capture the hiring manager's attention.

   Tailor your resume for each job

While it's tempting to blast out a generic resume to dozens of machine learning job postings, that approach is less effective. The best resumes are tailored for each specific job.

Study the job description carefully and mirror the language it uses. Does it call for specific machine learning skills, like NLP or computer vision? Or experience with certain industries, like healthcare or finance? Emphasize those things in your resume.

Here's an example of a strong, tailored bullets:

• Applied natural language processing techniques to analyze 50,000 patient records and identified 10 key factors contributing to hospital readmission risk

A well-tailored resume shows the hiring manager that you're a great fit for their specific role and increases your chances of landing an interview.

   Include relevant academic or personal projects

Many candidates applying for machine learning roles are recent graduates or self-taught practitioners. If you don't have much professional ML experience, academic or personal projects can be a great way to show your skills.

Choose projects that are substantial and relevant to the job you want. Describe them like this:

• Developed machine learning models to predict housing prices as part of master's thesis; achieved mean absolute error of $50,000 on test set
• Created sentiment analysis tool for tweets as personal project; trained on dataset of 1 million tweets and reached 90% accuracy

The hiring manager should be able to clearly understand the technology used, dataset size, and model performance. Group your best projects into a "Projects" section on your resume to make them easy to find.

   Demonstrate your machine learning process

Machine learning is a complex, iterative process involving data analysis, feature engineering, model selection, and evaluation. The best ML practitioners don't just jump straight to modeling, but take a rigorous approach to understand the data and problem first.

Communicate your machine learning process in your resume to show your expertise. For example:

• Cleaned and analyzed 5 GB of raw clickstream data to identify key factors driving customer purchase behavior
• Evaluated performance of 5 modeling approaches (linear regression, random forest, SVM, XGBoost, and neural network) using cross-validation
• Conducted error analysis to identify top opportunities for model improvement

This demonstrates that you understand the end-to-end machine learning workflow and follow best practices.

   Showcase ML projects with business impact

Ultimately, the goal of machine learning in most companies is to deliver business value, not just build cool models. The best machine learning resumes include projects that have real business impact.

Highlight times you've used machine learning to drive revenue, reduce costs, or improve key metrics. For example:

Developed machine learning system to optimize pricing of 10,000 SKUs, increasing gross margins by 3 percentage points and profits by $5M annually

Here's an example of a project description focused more on technical details than business outcomes:

Built product categorization model using multi-class logistic regression model with one-vs-rest architecture, achieving 92% accuracy on test set

While the technical details are important, make sure to tie them to the business goals. Show that you can select and implement machine learning solutions that deliver real value.

Writing Your Machine Learning Resume: Section By Section

  header, 1. put your name front and center.

Your name should be the most prominent element in your header, ideally on its own line. Use a larger font size than the rest of your header details to make it stand out.

Here's an example of a well-formatted name in a header:

Avoid formatting your name in a way that makes it harder to read or detracts from its prominence, such as:

• John K. Smith, MS

2. Include key contact details

In addition to your name, your header should include essential contact details so recruiters and hiring managers can easily get in touch with you. At a minimum, include:

• Phone number
• Professional email address
• Location (City, State)
• LinkedIn profile URL

You can include these details on a single line, separated by dividers. For example:

John Smith 415-555-1234 | [email protected] | San Francisco, CA | linkedin.com/in/johnsmith

3. Optionally include your top machine learning specialization

If you have a specific machine learning specialization that you want to highlight, consider including a one-line title or tagline under your name. This can help convey your focus area to recruiters at a glance.

For example:

• John Smith Machine Learning Engineer - Computer Vision Specialist
• Jane Doe Data Scientist | NLP and Deep Learning Expert

However, avoid cramming too many keywords or buzzwords into your title, as it can come across as inauthentic. A concise, targeted title is more effective than a long list of skills.

  Summary

A resume summary is an optional section that highlights your most impressive and relevant skills and experiences. While not mandatory, it can be beneficial for machine learning professionals to provide context and showcase their unique value proposition. Avoid using an objective statement, as it focuses on your goals rather than how you can contribute to the employer.

When crafting your summary, tailor it to the specific machine learning role you're targeting. Emphasize your technical expertise, key projects, and measurable achievements. Be concise and objective, using metrics to quantify your impact whenever possible.

To learn how to write an effective resume summary for your Machine Learning resume, or figure out if you need one, please read Machine Learning Resume Summary Examples , or Machine Learning Resume Objective Examples .

1. Highlight your machine learning expertise

Showcase your proficiency in machine learning techniques, algorithms, and tools that are relevant to the role. Mention specific skills such as:

• Deep learning frameworks (e.g., TensorFlow, PyTorch)
• Machine learning libraries (e.g., scikit-learn, Keras)
• Programming languages (e.g., Python, R)
• Data preprocessing and feature engineering

Example of highlighting machine learning expertise:

Machine Learning Engineer with 5+ years of experience developing and deploying deep learning models using TensorFlow and PyTorch. Skilled in data preprocessing, feature engineering, and model optimization. Proficient in Python and R programming.

2. Showcase impactful machine learning projects

Highlight 1-2 of your most impressive machine learning projects that demonstrate your ability to solve complex problems and drive business impact. Briefly describe the project objective, your role, the techniques used, and the measurable results achieved.

Example of a weak project description:

• Worked on a machine learning project to improve sales

Instead, provide specific details and outcomes:

• Developed a customer churn prediction model using XGBoost, reducing churn rate by 20% and saving $500K annually

3. Tailor your summary to the job

Customize your resume summary to the specific machine learning role and company you're targeting. Research the job requirements and company's projects to identify key skills and experiences to highlight.

Avoid using a generic, one-size-fits-all summary:

Experienced machine learning professional seeking a challenging role to grow my skills and contribute to a dynamic organization.

Instead, tailor your summary to the role:

Machine Learning Engineer with expertise in computer vision and deep learning, seeking to apply my skills to advance [Company X]'s autonomous vehicle projects. Proven track record of developing efficient CNNs and object detection models that improved accuracy by 30%.

  Experience

The work experience section is the heart of your machine learning resume. It's where you highlight your most relevant and impressive work, projects and achievements. Aim to tailor this section to the job description and focus on demonstrating the impact you've had in previous roles.

1. Highlight machine learning projects

Hiring managers want to see evidence of your machine learning skills and experience. Showcase 2-3 of your most impressive ML projects in your work experience section, even if they were part of an internship, course or side project.

When describing projects:

• Implemented CNN and RNN models in TensorFlow to classify customer support tickets, improving accuracy by 12%
• Developed recommendation engine in Python and AWS, increasing product upsells by 25%
• Built and deployed chatbot using LSTM and Python, reducing customer support volume by 30%

Whenever possible, quantify the business impact and results of your work. Use numbers, percentages and metrics to make your contributions concrete and measurable.

2. Demonstrate progression and leadership

Hiring managers look for candidates who have progressed in their career and taken on increasing responsibility. If you've been promoted, highlight that clearly. Use your work experience section to showcase your trajectory.

Compare these two examples:

• Software Engineer, ABC Company
• Senior Software Engineer, ABC Company
• Promoted to Senior Software Engineer, ABC Company

The second example makes your growth within the company clear. Similarly, if you've taken on any leadership responsibilities, like mentoring junior engineers or leading projects, make sure to call that out.

Highlighting your progression and leadership shows that you're driven and trusted by others. For more personalized feedback on your resume's trajectory, upload it to Targeted Resume to see how well it aligns with the job description.

3. Tailor your tools and technologies

Machine learning recruiters often look for specific tools and technologies, so make sure your resume reflects the key requirements listed in the job description. Some common ones in ML include:

• Programming languages: Python, R, Java, C++
• ML frameworks and libraries: TensorFlow, PyTorch, Keras, scikit-learn
• Cloud platforms: AWS, GCP, Azure
• Big data tools: Spark, Hadoop, Hive

Implemented machine learning models in Python using scikit-learn, TensorFlow and Keras, and deployed using Docker on AWS EC2

Weave in mentions of relevant tools throughout your work experience. This quickly shows the hiring manager that you have the required technical skills for the role.

4. Focus on results and impact

The most effective machine learning resumes focus on results, not just responsibilities. Hiring managers don't just want to know what you did in previous roles - they want to know the impact you had.

Consider the difference between these two bullet points:

• Built and trained machine learning models in TensorFlow
• Built and trained CNN model in TensorFlow to detect product defects, reducing scrap rate by 15% and saving $500K annually

The second bullet point is much stronger because it focuses on the business impact. Whenever possible, quantify your achievements with hard numbers. Think about metrics like:

• Accuracy improvements
• Runtime reductions
• Cost savings
• Revenue generation

If you're having trouble quantifying your impact, consider including numbers like dataset size, model training time or lines of code written. Any specifics you can provide will strengthen your resume.

  Education

The education section of your machine learning resume should be concise yet impactful. It's an opportunity to showcase your relevant academic background and specialized training that prepared you for a career in this field. Let's break down the key elements to include and how to present them effectively.

1. Highlight your degree and major

Start by listing your highest degree earned, such as a Bachelor's or Master's, followed by your major or field of study. If your degree is directly related to machine learning or a closely related field like computer science or statistics, make sure to emphasize that.

• Master of Science in Computer Science, Specialization in Machine Learning
• Bachelor of Science in Statistics, Minor in Artificial Intelligence

Avoid listing degrees or majors that are not relevant to the field, as they can distract from your core qualifications.

2. Include relevant coursework and projects

If you're a recent graduate or have limited work experience, highlighting relevant coursework and academic projects can demonstrate your knowledge and hands-on experience with machine learning concepts and tools.

Examples of coursework to include:

• Machine Learning Algorithms
• Deep Learning
• Natural Language Processing
• Big Data Analytics

Avoid listing basic or introductory courses that are expected of all graduates in your field.

3. Showcase your certifications

In the rapidly evolving field of machine learning, certifications can demonstrate your commitment to staying up-to-date with the latest technologies and best practices. Include any relevant certifications you've earned, such as:

• TensorFlow Developer Certificate
• AWS Certified Machine Learning – Specialty
• Google Cloud Professional Machine Learning Engineer

Be sure to list the full name of the certification, the issuing organization, and the date earned.

4. Keep it concise for senior-level roles

If you're a senior-level machine learning professional with extensive work experience, your education section should be brief and to the point. Hiring managers will be more interested in your professional accomplishments and impact.

A concise education section for a senior-level candidate might look like this:

Ph.D. in Computer Science, XYZ University M.S. in Machine Learning, ABC University

In contrast, avoid listing outdated or irrelevant information like this:

Ph.D. in Computer Science, XYZ University, 1995-2000 Dissertation: [Title] GPA: 3.8/4.0 Relevant Coursework: [List] Teaching Assistant for Intro to Programming, 1998-1999

Action Verbs For Machine Learning Resumes

Even though this is a highly technical role, you should still demonstrate excellent communication skills. Your resume should be direct and precise. That’s why you should take care of your word economy since you have limited space. This is where action verbs can help you; they provide an accurate description of your duties or accomplishments in just a single word. 

You should ideally use different vocabulary on your resume that is highly related to your profession. That’s why we have selected a list of action verbs for your machine-learning resume. This will help you improve your readability and seamlessly describe your professional experience.

• Strengthened
• Troubleshooted

For a full list of effective resume action verbs, visit Resume Action Verbs .

Action Verbs for Machine Learning Resumes

Skills for machine learning resumes.

A machine learning engineer should have a combination of data science and software engineering skills. This is what you should demonstrate in your resume. Mention the programming languages you are familiar with, such as SQL, Python, R, and C. Additionally, you should include ML fundamental skills such as deep learning, data structures, and neural networks.

Your machine learning resume should focus on technical skills. You can demonstrate your soft skills throughout your resume, but it’s better to maintain the skills section focused on technical competencies. If you’d like to know more ideas to include in your resume, take a look at our selection of hard skills for machine learning engineers. 

• Machine Learning
• Python (Programming Language)
• Computer Vision
• Artificial Intelligence (AI)
• Natural Language Processing (NLP)
• Data Mining
• Data Science
• Apache Spark
• Image Processing
• Pattern Recognition
• Scikit-Learn
• Neural Networks
• Pandas (Software)
• Artificial Neural Networks
• Amazon Web Services (AWS)

Skills Word Cloud For Machine Learning Resumes

This word cloud highlights the important keywords that appear on Machine Learning job descriptions and resumes. The bigger the word, the more frequently it appears on job postings, and the more 'important' it is.

How to use these skills?

Other data & analytics resumes, instructional design.

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Machine Learning Resume Guide

• GIS Resume Guide
• Machine Learning Engineer Resume Example
• Machine Learning Intern Resume Example
• Senior Machine Learning Engineer Resume Example
• NLP (Natural Language Processing) Engineer Resume Example
• Artificial Intelligence Specialist Resume Example
• Tips for Machine Learning Resumes
• Skills and Keywords to Add
• All Resume Examples
• Machine Learning CV Examples
• Machine Learning Cover Letter
• Machine Learning Interview Guide
• Explore Alternative and Similar Careers

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Best Machine Learning Projects With Source Code [2024]

Introduction, uses of machine learning, 1. image recognition, 2. voice recognition, 3. prediction in travel, 4. video surveillance, 5. social media platform, 6. spam and malware, top machine learning projects, machine learning projects for beginners, 1. home value prediction project, 2. sales prediction project, 3. music recommendation system project, 4. iris flowers classification ml project, 5. stock prices predictor with the help of timeseries, 6. predicting wine quality with the help of wine quality dataset, 7. mnist (modified national institute of standards and technology) handwritten digit classification, intermediate machine learning projects, 8. finding frauds when tracking imbalanced data, 9. market basket analysis, 10. text summarisation, 11. black friday sales prediction, 12. million song analysis, 13. movie recommendation engine, advance machine learning projects, 14. catching crooks on the hook, 15. uber helpful customer support, 16. barbie with brains, 17. netflix artwork personalization, 18. myers-briggs personality prediction, 19. youtube comment analysis, q.1 why is ml interesting, q.2 what are some of the machine learning projects for students, q.3 what is the future of machine learning, additional resources.

This technology-driven world has seen the emergence of machine learning. It is an outstanding field that enables our machines or electronic device to become more intelligent. The main aim of this field is to remodel a simplistic machine into a machine having the ability of the mind. The best way to know about this technology is by working on projects. Other options include online machine learning courses , browsing through books, which only aid in learning the fundamentals of ML, but it is only plausible to learn the subject in depth by working on projects with real-world data. Some of these projects have comparable data sets that can be found on Kaggle. One can utilize these datasets to finish the projects and acquire new skills in the field of ML. These projects are best applicable for you if you are an amateur or in the intermediate phase and still studying more about Machine Learning. In case you are up for more high-level challenges, you can always discover more complex projects on Kaggle. Machine learning involves these stages

• Input a machine learning algorithm examples of input data and a list of likely tags for that particular input.
• The input data is modified into text vectors, a collection of numbers that denote various data features.
• Algorithms learn to associate feature vectors with tags on the basis of manually tagged samples, and automatically create predictions while processing unseen data.

While AI and ML are usually utilized interchangeably, they are two distinct theories. Artificial Intelligence is the more comprehensive concept – machines composing decisions, acquiring new skills, and then resolving problems for humans. On the other hand, machine learning is a subset of AI that allows intelligent systems to learn new things from data autonomously. In this article, let us learn more about machine learning projects to boost your interest. These ML projects are very competitive, critical, and interesting to create. It is firmly believed that these projects are the most desirable place to invest your skill and time. Let’s dive deep into some interesting, unique as well as simple machine-learning projects.

There are endless uses of machine learning and there are several machine learning algorithms that are available for you to learn. They can be found in every form from simple to extremely complex. Listed below are a few uses of Machine Learning

Confused about your next job?

Image recognition is considered one of the most popular uses of machine learning applications. It can also be regarded as a digital image and for these images, the measurement represents the yield of each pixel in an image. Face recognition is also an example of an excellent trait of ML, it helps to identify the face, and the notifications are sent related to that to people.

Machine learning also assists in creating the application for voice recognition. It is also regarded as a virtual personal assistant (VPA). It will help you to look for the information when asked over the voice. After you have asked your question over voice, that assistant will search for the data that has been asked by you and accumulate the necessary information to present you with the most suitable answer. There are several Machine learning tools for voice recognition like Amazon Echo and google home smart speakers.

Predictions assist in developing applications that foretell the price of a cab or travel for a selected time and also inform beforehand about the congestion of traffic. All of these are possible only because of ML. While booking the cab, with the help of ML, the app calculates the estimated amount of the trip which is done with the help of machine learning only. When we use GPS service to know about the route from source to destination, the application will mark different routes and will also check the traffic at that moment. It will also let you know where the congestion of traffic is.

Video Surveillance helps you to identify the crime or any mishaps that might happen before it has actually happened with the help of machine learning. It allows you to track the unusual demeanour of people like sleeping on benches and standing still for a prolonged time, etc. An automatic alert is generated to the guards and they can help to evade any issues or problems.

Social Media is used for giving more conforming news feeds and advertisements according to the interest of the user and this happens through the use of machine learning. There are several examples like friends and page recommendations, video, and song suggestions on YouTube that are done with the help of machine learning. It essentially runs on the user’s experience, like with whom are they getting connected, and who visits the profiles very often, and accordingly the recommendations are provided to the user. It also gives you the technique to obtain insightful information from images and videos. Machine Learning makes YouTube more seamless.

Email clients employ several spam filtering and these spam filters are updated constantly. These are essentially done with the help of machine learning. Tree induction, Rule-based and multi-layer, and a few of the techniques that are rendered by machine learning. Furthermore, some malware is recognized and these are identified chiefly by the system security programs that are assisted by machine learning only.

Let us now look at 20 machine-learning project ideas for beginners, intermediates, and experts to attain the real-world experience of this thriving technology in 2023.

Take a situation into consideration where you wish to buy/sell a house, or you are relocating to a new city and you are looking for a rented house.

In this project, you will handle the dataset to develop a house price prediction model with XGBoost. The factors that are taken into consideration are average income, number of hospitals, number of schools, crime rate, etc.

Source Code: House Value Prediction

As a novice, you should work on various machine learning project ideas to expand your skillset. This dataset comprises 2013 sales data for 1559 products beyond 10 different outlets in different cities. The goal is to develop a regression model to foretell the sales of all 1559 products for the subsequent year in all of the 10 different BigMart outlets.

Source Code: Sales Prediction

Based on the songs you’ve liked, Spotify will show similar songs that you may like. How does the system do this? This is a perfect example of where ML can be applied. The initial task is to foretell the possibilities of a user listening to a song on loop within a time frame. In the dataset, the prediction is regarded as 1 if the user has heard the same song within a month. The dataset comprises a list of songs that have been heard by which consumer and at what time.

Source Code: Music Recommendation Project

Iris Flowers is one of the most simplistic machine learning datasets in classification literature. This machine learning problem is usually regarded as the “Hello World” of machine learning. The dataset has numeric traits and Machine Learning beginners need to comprehend how to handle and load data.

You can download Iris Dataset from UCI ML Repository Download Iris Flowers Dataset

This is another fascinating machine learning project idea in the finance domain. A stock prices predictor determines the performance of a company and foretells future stock prices.

A time series is an interpretation of event occurrences over a span of time. A time series is investigated to recognize patterns so that future incidents can be foretold on the basis of trends witnessed over a span of time. Some of the models that can be applied for time series forecasting include ARIMA (autoregressive integrated moving average), moving average, and exponential smoothing.

Source Code: Stock Prices Predictor

The principal purpose of this ML project is to develop a machine learning model to foretell the quality of wines by investigating their different chemical properties. The dataset of wine quality comprises 4898 observations with 1 dependent variable and 11 independent variables.

Source Code: Wine Quality Prediction

Deep learning plays an important role in the recognition of images, even self-driving cars, and automatic text generation. To start operating in these areas, you require to start with a simplistic and easy dataset like the MNIST dataset. The MNIST Handwritten Digit Classification dataset is extremely small to fit into your PC memory and is also beginner-friendly. Here is a free Deep Learning course that you can consider to get started with the fundamentals of Deep Learning.

Source code in Python: MNIST Handwritten Digit Classification Project

Owing to the growing financial crime, the value of AI-powered fraud detection is more prominent than ever. Fraud detection is a division problem that works with imbalanced data, indicating that the fraud to be predicted is in the minority. Predictive models often strive to create real business value from imbalanced data, and the conclusions may be inaccurate.

To discuss the issue, you can incorporate three separate strategies:

• Oversampling
• Undersampling
• A combined approach

In this project, you can use an apriori algorithm to explain and foretell consumer purchasing behaviors, commonly known as Market Basket Analysis. According to the principles of Market Basket Analysis, if a consumer purchases a certain group of items, that customer is expected to buy similar items as well. Learn more about this in the Kaggle dataset.

Source – Market Basket Analysis

Text summarization summarises a part of the text while conserving its meaning. Extractive text summarization employs a scoring function to recognize and pick important pieces of text from a document and compile them into an edited version of the original. Abstractive text summarization utilizes high-level natural language processing techniques to create a new, shorter version that conveys the same information.

For this, you will need to know about Pandas, Numpy, and NTLK. You’ll Discover a step-by-step model to text summarization system building here.

Source Code – Text Summarization

The dataset includes demographic information for consumers that includes age, marital status, gender, location, and more, as well as commodity details and complete purchase amounts.

Varying from emails to social media posts, 80 percent of extant text data is not structured. Text mining is a way to extract valuable insights from this type of raw data. The method of text mining converts unstructured text data into a structured format, promoting the identification of important patterns and associations within data sets.

To give text mining a try, experiment with these publicly available text data sets .

Source Code – Black Friday Sales Prediction

Apply this subset of the Million Song Dataset to foretell the song’s release year its audio features. The songs are fundamentally commercial Western tracks ranging from 1922 to 2011. The focus of the dataset is feature analysis and metadata associated with each track.

Netflix employs collaborative filtering as part of its complicated recommendation system. Similarly, MovieLens Dataset can help you. Collaborative filtering recommendation engines interpret consumer behavior, preferences, and associations between consumers to foretell what users will like.

Global Fishing Watch recognizes and traces illegal fishing activity by collecting GPS data from ships and processing GPS data and different pieces of information with neural networks. The website’s algorithm can distinguish these ships by type, fishing gear, and fishing behaviors.

Download Global Fishing Watch datasets here .

To solve consumer issues with effectiveness and expertise, Uber created a machine learning tool called COTA (Customer Obsession Ticket Assistant). It processes consumer support tickets with the help of “human-in-the-loop” model architecture. Basically, COTA employs machine learning and natural language processing techniques to classify tickets, recognize ticket issues, and recommend solutions.

Talking dolls that repeat previously recorded phrases are nothing unusual. But Hello Barbie uses natural language processing and high-level audio analytics that allow the doll to communicate reasonably in conversation. With one button sensibly engineered into her outfit, Hello Barbie is able to tape conversations and upload them to servers run by ToyTalk, where the data was investigated.

Netflix personalizes the artwork and imagery used to convey title recommendations to consumers. The aim is to show you what you like, Netflix applies a convolutional neural network that interprets visual imagery. The company relies on “contextual bandits,” which work continually to decide which artwork gets better engagement.

The Myers Briggs Type Indicator is a famous character test that separates people into 16 distinct personality types beyond 4 axes. With the help of this Kaggle dataset, you can assess the effectiveness of the test and try to recognize patterns associated with personality type and writing style Every row in this dataset includes a person’s Myers-Briggs personality type accompanied with examples of their writing.

If you want to examine YouTube comments with natural language processing techniques, begin by scraping your text data by giving leverage to a library like Youtube-Comment-Scraper-Python. It fetches YouTube video comments utilizing browser automation.

Understanding machine learning and deep learning notions are important. No project proceeds successfully without substantial planning, and machine learning is no exception. Developing your first machine learning project is not as tough as it seems given you have a solid planning strategy. To begin any ML project, one must develop a complete end-to-end approach -beginning from scoping projects to model deployment and management in production. Thus, incorporate these machine learning projects into your resume and land a top gig with a greater salary and worthwhile perks.

Answer: Machine learning is interesting as programs learn from examples. From the data that you have accumulated, a machine learning method can investigate automatically and know about the structure already resident in that data to render a solution to the problem you are attempting to resolve.

Answer : Some of the machine learning projects for students are:

• Stock Prices Prediction
• Sales Forecasting
• Movie Ticket Pricing Prediction
• Music Recommendation
• Sentiment Analysis of Product Reviews

Answer: The future of machine learning is very alluring. Currently, every popular domain is powered by ML applications. To mention some of the realms, healthcare, education, search engine, and digital marketing, are the major beneficiaries.

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Top python projects for beginners to advanced [with source code], top 20 iot projects with source code [2024].

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5 Real Machine Learning Resume Examples Built for 2024

• Machine Learning (ML) Resume
• ML Engineer
• Deep Learning
• Senior ML Engineer
• Write Your Machine Learning Resume

When you try to explain your job to people, most of them just think you’re a tech wizard—and they’re not wrong. Working in machine learning, you use complex algorithms to create intelligent systems that learn from data and continually improve over time.

Few industries are as fast-paced as machine learning right now, and there’s a huge demand for experts like you. To get the best offer possible, you’ll need to write a resume that effectively highlights your strengths. 

We get it—keeping up with the world of machine learning is quite time-consuming, so we’re here to help you score your next job. Check out our real machine-learning resume examples and tips for writing an effective cover letter !

Machine Learning Resume

or download as PDF

Why this resume works

• Create a lasting experience for hiring managers by using the “Elegant” template and warm colors that are easy on the eyes. Now, structure your work experience chronologically and add them along with all your best skills on the left-hand side. A simple resume that highlights all your technology expertise is more than enough here!

Machine Learning Engineer Resume

• A good number of candidates spend years after college to land a job. Showcase your passion for this field by adding the first place you worked at, even if it was only as a data analyst. As long as you can pull up relevant metrics, you’ll be all set.

Deep Learning Resume

• A small role such as a research assistant can also do wonders if you’re able to mention your impact on the company’s process. Use quantifiable bullet points like, “21.3% improvement in product safety as per risk assessments” that speak volumes about your skills in deep learning right from the get-go.

Machine Learning Intern Resume

• Your employer has to go through hundreds of applicants who are just fresh out of college. There’s only one way your machine learning intern resume can stand out. Clearly align your objective to state your past project experience and contributions along with your reason to apply at the company.

Senior Machine Learning Engineer Resume

• Use each work experience to your advantage here. Show your dates for each role to convey how you’ve managed to become a senior in this field within a couple of years. Add more flair to your senior machine learning engineer resume by listing your versatility in using multiple software.

Related resume examples

• Software engineer
• Computer science
• Data analyst
• Data engineer

Show Off the Right Skills In Your Real Machine Learning Resume

You’ve built up a skill set that’s highly desired in today’s job market. Now, you get to put those job skills to work by designing, testing, and analyzing machine learning models with the greatest possible accuracy.

Given the specialized and technical nature of your line of work, use this section to really get into the nitty-gritty of what makes you an ML expert. 

For instance, if you manage a team, mention planning and overseeing team progress in Jira. Similarly, if you’re a deep learning specialist, talk about your mastery of TensorFlow and Keras.

Need some ideas?

15 top real machine learning skills

• Apache Kafka
• Apache Hadoop
• Jupyter Notebook
• Python/Java
• AWS/Azure/GCP
• Scikit-Learn

Your real machine learning work experience bullet points

You develop machine learning models and algorithms with frameworks like TensorFlow and PyTorch. Next, you analyze data on Pandas. Lastly, just for good measure, you’ll document some code and model performance. That’s a lot, and that’s just your daily grind.

To distinguish yourself, however, talk about your achievements rather than your day-to-day work. And to make them really catch a recruiter’s eye, back them up with quantifiable metrics.

For instance, talk about how the model you developed on Python improved predictive accuracy for business processes or how much your TensorFlow model reduced algorithm runtimes.

• Highlight how resolving code issues led to improved stability or reduced occurrences of system crashes or data loss.
• Emphasize the time and cost savings that the implementation of your machine learning models resulted in.
• Mention how data processing systems or scripts you implemented improved efficiency, data entry or processing speeds, and reduced manual error checks.
• Discuss how your optimization strategies improved prediction model accuracy, boosted benchmarks, or shrank biases.

See what we mean?

• Resolved complex CUDA code issues, increasing the stability of machine learning applications and reducing incidents of system crashes by 83%
• Innovated with Google AutoML to refine acoustic signal processing in Moog’s testing facilities, improving the detection of ultrasonic frequencies by 79.4%
• Engineered a Flask-based REST API for an analytics dashboard, leading to a 27% increase in back-end efficiency and a 19% reduction in load times
• Implemented an OpenCV-powered video analysis tool that reduced content classification time by 2.6 hours, enhancing content discovery features

9 active verbs to start your real machine learning work experience bullet points

• Implemented
• Spearheaded
• Streamlined
• Collaborated

3 Tips for Writing a Real Machine Learning Resume as a Budding ML Engineer

• Programming is a vital part of machine learning. To show recruiters you’re up to the task, highlight your proficiency in commonly used programming languages in the field, such as Python, Java, or R.
• If you have a degree in ML, CS, or a related field, highlight this in your resume, including any relevant projects or research you conducted during your studies. This will show employers what you’re capable of.
• Internships can provide you with a lot of valuable work experience , so talk about projects you’ve done that were related to machine learning. Discuss those you participated in and what you were able to learn.

3 Tips for Writing a Real Machine Learning Resume as a Senior Engineer

• To make your resume stand out, provide quantifiable metrics and results of your machine learning projects. Talk about how your work had a positive impact on KPIs such as model accuracy, efficiency, or performance, reinforcing your ability to deliver real results.
• If you’ve worked on any machine learning research or publications, highlight them in your resume. This could be anything from presenting at conferences, co-authoring papers, or being involved in cutting-edge research. 
• If you specialize in a niche within machine learning, such as natural language processing, deep learning, or machine learning architecture, dive into its details. Talk about advanced courses, certifications, or workshops you’ve completed to reaffirm your expertise.

Talk about times when you took control of complex machine-learning projects. Discuss how you managed project timelines and made critical decisions, working with both non-technical and technical stakeholders.

Staying current in your line of work is crucial. Show recruiters that you’re up to date by mentioning any conferences, advanced courses, or workshops you’ve attended. Doing this highlights your dedication to your professional growth in the industry.

Try and stick to a single-page resume . If you have more than a decade of experience in machine learning, it’s fine to stretch this to two pages—however, even if this is the case, include only your most recent and relevant experience. 

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16 Data Science Projects with Source Code to Strengthen your Resume

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Tried to build some data science projects to improve your resume and got intimidated by the size of the code and the number of concepts used? Does it feel too out of reach, and did it crush your dreams of becoming a data scientist? We have collected for you sixteen data science projects with source code so you can actually participate in the real-time projects of data science. These will help boost confidence and also tell the interviewer that you’re serious about data science.

Do you know?

Finding a perfect idea for your project is something that concerns you more than implementing the project itself, isn’t it? So keeping the same in mind, we have compiled a list of over 500+ project ideas just for you. All you have to do is bookmark this article and get started.

• Python Projects
• Python Django (Web Development) Projects
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In this blog, we will list out different data science project examples in the languages R and Python. Let’s separate these on the basis of difficulty so you have a proper path to follow.

Top Data Science Project Ideas

Here are the best data science project ideas with source code:

1. Beginner Data Science Projects

1.1 fake news detection.

Drive your career to new heights by working on Data Science Project for Beginners  –  Detecting Fake News with Python

A king of yellow journalism, fake news is false information and hoaxes spread through social media and other online media to achieve a political agenda. In this data science project idea, we will use Python to build a model that can accurately detect whether a piece of news is real or fake. We’ll build a TfidfVectorizer and use a PassiveAggressiveClassifier to classify news into “Real” and “Fake”. We’ll be using a dataset of shape 7796×4 and execute everything in Jupyter Lab.

Language:  Python

Dataset/Package:  news.csv

1.2 Road Lane Line Detection

Check the complete implementation of Lane Line Detection Data Science Project:  Real-time Lane Line Detection in Python

Data Science Project Idea:  The lines drawn on the roads guide human drivers where the lanes are. It also refers to the direction to steer the vehicle. This application is cardinal for developing driverless cars.

You can build an application having the ability to identify track lines from input images or continuous video frames.

1.3 Sentiment Analysis

Check the complete implementation of Data Science Project with Source Code –  Sentiment Analysis Project in R

Sentiment analysis is the act of analyzing words to determine sentiments and opinions that may be positive or negative in polarity. This is a type of classification where the classes may be binary (positive and negative) or multiple (happy, angry, sad, disgusted,..). We’ll implement this data science project in the language R and use the dataset by the ‘janeaustenR’ package. We will use general-purpose lexicons like AFINN, bing, and loughran, perform an inner join, and in the end, we’ll build a word cloud to display the result.

Language:  R

Dataset/Package:  janeaustenR

1.4 Detecting Parkinson’s Disease

Put your best foot forward by working on Data Science Project Idea –  Detecting Parkinson’s Disease with XGBoost

We have started using data science to improve healthcare and services – if we can predict a disease early, it has many advantages on the prognosis. So in this data science project idea, we will learn to detect Parkinson’s Disease with Python. This is a neurodegenerative, progressive disorder of the central nervous system that affects movement and causes tremors and stiffness. This affects dopamine-producing neurons in the brain and every year, it affects more than 1 million individuals in India.

Language:  Python

Dataset/Package:  UCI ML Parkinsons dataset

1.5 Color Detection with Python

Build an application to detect colors with Beginner Data Science Project –  Color Detection with OpenCV

How many times has it occurred to you that even after seeing, you don’t remember the name of the color? There can be 16 million colors based on the different RGB color values but we only remember a few. So in this project, we are going to build an interactive app that will detect the selected color from any image. To implement this we will need a labeled data of all the known colors then we will calculate which color resembles the most with the selected color value.

Dataset:  Codebrainz Color Names

1.6 Brain Tumor Detection with Data Science

Data Science Project Idea:  There are many famous deep learning projects on MRI scan dataset. One of them is Brain Tumor detection. You can use transfer learning on these MRI scans to get the required features for classification. Or you can train your own convolution neural network from scratch to detect brain tumors.

Dataset:  Brain MRI Image Dataset

1.7 Leaf Disease Detection

Data Science Project Idea:  Disease detection in plants plays a very important role in the field of agriculture. This Data Science project aims to provide an image-based automatic inspection interface. It involves the use of self designed image processing and deep learning techniques. It will categorize plant leaves as healthy or infected.

Dataset:  Leaf Dataset

2. Intermediate Data Science Projects

2.1 speech emotion recognition.

Explore the complete implementation of Data Science Project Example  –  Speech Emotion Recognition with Librosa

Let’s learn to use different libraries now. This data science project uses librosa to perform Speech Emotion Recognition. SER is the process of trying to recognize human emotion and affective states from speech. Since we use tone and pitch to express emotion through voice, SER is possible; but it is tough because emotions are subjective and annotating audio is challenging. We’ll use the mfcc, chroma, and mel features and use the RAVDESS dataset to recognize emotion on. We’ll build an MLPClassifier for the model.

Dataset/Package:  RAVDESS dataset

2.2 Gender and Age Detection with Data Science

Put the pedal to the metal & impress recruiters with ultimate Data Science Project –  Gender and Age Detection with OpenCV

This is an interesting data science project with Python. Using just one image, you’ll learn to predict the gender and age range of an individual. In this, we introduce you to Computer Vision and its principles. We’ll build a  Convolutional Neural Network   and use models trained by Tal Hassner and Gil Levi for the Adience dataset. We’ll use some  .pb, .pbtxt, .prototxt, and .caffemodel  files along the way.

Dataset/Package:  Adience

2.3 Diabetic Retinopathy

Data Science Project Idea:  Diabetic Retinopathy is a leading cause of blindness. You can develop an automatic method of diabetic retinopathy screening. You can train a neural network on retina images of affected and normal people. This project will classify whether the patient has retinopathy or not.

Dataset:  Diabetic Retinopathy Dataset

2.3 Uber Data Analysis in R

Check the complete implementation of Data Science Project with Source Code –  Uber Data Analysis Project in R

This is a data visualization project with ggplot2 where we’ll use R and its libraries and analyze various parameters like trips by the hours in a day and trips during months in a year. We’ll use the Uber Pickups in New York City dataset and create visualizations for different time-frames of the year. This tells us how time affects customer trips.

Dataset/Package:  Uber Pickups in New York City dataset

2.4  Driver Drowsiness detection in Python

Drive your career to new heights by working on Top Data Science Project  –  Drowsiness Detection System with OpenCV & Keras

Drowsy driving is extremely dangerous and around thousands of accidents happen each year due to drivers falling asleep while driving. In this Python project, we will build a system that can detect sleepy drivers and also alert them by beeping alarm.

This project is implemented using Keras and OpenCV. We will use OpenCV for face and eye detection and with Keras, we will classify the state of the eye (Open or Close) using Deep neural network techniques.

2.5 Chatbot Project in Python

Build a chatbot using Python & step up in your career –  Chatbot with NLTK & Keras

Chatbots are an essential part of the business. Many businesses has to offer services to their customers and it needs a lot of manpower, time and effort to handle customers. The chatbots can automate most of the customer interaction by answering some of the frequent questions that are asked by the customers. There are mainly two types of chatbots: Domain-specific and Open-domain chatbots. The domain-specific chatbot is often used to solve a particular problem. So you need to customize it smartly to work effectively in your domain. The Open-domain chatbots can be asked any type of question so it requires huge amounts of data to train.

Dataset:  Intents json file

2.6 Handwritten Digit Recognition Project

Practically implement the Deep Learning Project with Source Code –  Handwritten Digit Recognition with CNN

The MNIST dataset of handwritten digits is widespread among the data scientists and machine learning enthusiasts. It is an amazing project to get started with the data science and understand the processes involved in a project. The project is implemented using the Convolutional Neural Networks and then for real-time prediction we also build a nice graphical user interface to draw digits on a canvas and then the model will predict the digit.

Dataset:  MNIST

Get hired as a data scientist with  Top Data Science Interview Questions

3. Advanced Data Science Projects

3.1 image caption generator project in python.

This is an interesting data science project. Describing what’s in an image is an easy task for humans but for computers, an image is just a bunch of numbers that represent the color value of each pixel. So this is a difficult task for computers to understand what is in the image and then generating the description in Natural language like English is another difficult task. This project uses deep learning techniques where we implement a Convolutional neural network (CNN) with Recurrent Neural Network( LSTM) to build the image caption generator.

Dataset:  Flickr 8K

Framework:  Keras

3.2 Credit Card Fraud Detection Project

Put your best foot forward by working on Data Science Projects  –  Credit Card Fraud Detection with Machine Learning

By now, you’ve begun to understand the methods and concepts. Let’s move on to some advanced data science projects. In this project, we’ll use R with algorithms like  Decision Trees , Logistic Regression, Artificial Neural Networks, and Gradient Boosting Classifier. We’ll use the Card Transactions dataset to classify credit card transactions into fraudulent and genuine. We’ll fit the different models and plot performance curves for them.

Dataset/Package:  Card Transactions dataset

3.3 Movie Recommendation System

Explore the implementation of the Best Data Science Project with Source Code-  Movie Recommendation System Project in R

In this data science project, we’ll use R to perform a movie recommendation through machine learning. A recommendation system sends out suggestions to users through a filtering process based on other users’ preferences and browsing history. If A and B like Home Alone and B likes Mean Girls, it can be suggested to A – they might like it too. This keeps customers engaged with the platform.

Dataset/Package:  MovieLens dataset

3.4 Customer Segmentation

Put the medal to the pedal & impress recruiters with Data Science Project (Source Code included) –  Customer Segmentation with Machine Learning

This is one of the most popular projects in Data Science. Before running any campaign companies create different groups of customers.

Customer Segmentation is a popular application of unsupervised learning. Using clustering, companies identify segments of customers to target the potential user base. They divide customers into groups according to common characteristics like gender, age, interests, and spending habits so they can market to each group effectively. We’ll use  K-means clustering  and also visualize the gender and age distributions. Then, we’ll analyze their annual incomes and spending scores.

Dataset/Package:  Mall_Customers dataset

3.5 Breast Cancer Classification

Check the complete implementation of Data Science Project in Python –  Breast Cancer Classification with Deep Learning

Coming back to the medical contributions of data science, let’s learn to detect breast cancer with Python. We’ll use the IDC_regular dataset to detect the presence of Invasive Ductal Carcinoma, the most common form of breast cancer. It develops in a milk duct invading the fibrous or fatty breast tissue outside the duct. In this data science project idea, we’ll use  Deep Learning  and the Keras library for classification.

Dataset/Package:  IDC_regular

3.6 Traffic Signs Recognition

Achieve accuracy in self-driving cars technology with Data Science Project on  Traffic Signs Recognition using CNN  with Source Code 

Traffic signs and rules are very important that every driver must follow to avoid any accident. To follow the rule one must first understand how the traffic sign looks like. A human has to learn all the traffic signs before they are given the license to drive any vehicle. But now autonomous vehicles are rising and there will be no human drivers in the upcoming future. In the Traffic signs recognition project, you will learn how a program can identify the type of traffic sign by taking an image as input. The German Traffic signs recognition benchmark dataset (GTSRB) is used to build a Deep Neural Network to recognize the class a traffic sign belongs to. We also build a simple GUI to interact with the application.

Dataset:  GTSRB (German Traffic Sign Recognition Benchmark)

The source code of all these data science projects is available on DataFlair. Get started now and build a project in Data Science. Follow from beginner to advanced, and once you’re done, you can move on to other projects.

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Analytics Insight

Top 10 Machine Learning Projects to Boost Your Resume

These machine learning projects will bring your resume to the top.

Sales forecasting, customer service chatbot.

• Rule-Based Chatbots — These chatbots aren’t intelligent. They are fed a set of pre-defined rules and only reply to users based on these rules. Some chatbots are also provided with a pre-defined set of questions and answers and cannot answer queries that fall outside this domain.
• Independent Chatbots — Independent chatbots utilize machine learning to process and analyze a user’s request and provide responses accordingly.
• NLP Chatbots — These chatbots can understand patterns in words and distinguish between different word combinations. They are the most advanced of all three chatbot types, as they can come up with what to say next based on the word patterns they were trained on.

Wildlife Object Detection System

• Install cameras in the area you want to monitor.
• Download all video footage and save them.
• Create a Python application to analyze incoming images and identify wild animals.

Spotify Music Recommender System

Market basket analysis, nyc taxi trip duration, real-time spam detection, myers-briggs personality prediction app.

• Build a multi-class classification model and train it on the Myers-Briggs dataset on Kaggle. This involves data pre-processing (removing stop-words and unnecessary characters) and some feature engineering. You can use a shallow learning model like logistic regression or a deep learning model like an LSTM for this purpose.
• You can create an application that allows users to enter any sentence of their choice.
• Save your machine learning model weights and integrate the model with your app. After the end-user enters a word, display their personality type on the screen after the model makes a prediction.

Mood Recognition System + Recommender System

• Create an app that can take in a live video feed.
• Use Python’s face recognition API to detect faces and emotions on objects in the video feed.
• After classifying these emotions into various categories, start building the recommender system. This can be a set of hardcoded values for each emotion, which means you don’t need to involve machine learning for the recommendations.
• Once you’re done building the app, you can deploy it on Heroku, Dash, or a web server.

YouTube Comment Sentiment Analysis

• Scrape comments of the videos by the YouTubers you want to analyze.
• Use a pre-trained sentiment analysis model to make predictions on each comment.
• Visualize the model’s predictions on a dashboard. You can even create a dashboard app using libraries like Dash (Python) or Shiny (R).
• You can make the dashboard interactive by allowing users to filter sentiment by time frame, name of YouTuber, and video genre.

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• Machine Learning Tutorial
• Data Analysis Tutorial
• Python - Data visualization tutorial
• Machine Learning Projects
• Machine Learning Interview Questions
• Machine Learning Mathematics
• Deep Learning Tutorial
• Deep Learning Project
• Deep Learning Interview Questions
• Computer Vision Tutorial

Computer Vision Projects

• NLP Project
• NLP Interview Questions
• Statistics with Python
• 100 Days of Machine Learning

100+ Machine Learning Projects with Source Code [2024]

Classification projects.

• Wine Quality Prediction - Machine Learning
• ML | Credit Card Fraud Detection
• Disease Prediction Using Machine Learning
• Recommendation System in Python
• Detecting Spam Emails Using Tensorflow in Python
• SMS Spam Detection using TensorFlow in Python
• Python | Classify Handwritten Digits with Tensorflow
• Recognizing HandWritten Digits in Scikit Learn
• Identifying handwritten digits using Logistic Regression in PyTorch
• Python | Customer Churn Analysis Prediction
• Online Payment Fraud Detection using Machine Learning in Python
• Flipkart Reviews Sentiment Analysis using Python
• Loan Approval Prediction using Machine Learning
• Loan Eligibility prediction using Machine Learning Models in Python
• Stock Price Prediction using Machine Learning in Python
• Bitcoin Price Prediction using Machine Learning in Python
• Handwritten Digit Recognition using Neural Network
• Parkinson Disease Prediction using Machine Learning - Python
• Spaceship Titanic Project using Machine Learning - Python
• Rainfall Prediction using Machine Learning - Python
• Autism Prediction using Machine Learning
• Predicting Stock Price Direction using Support Vector Machines
• Fake News Detection Model using TensorFlow in Python
• CIFAR-10 Image Classification in TensorFlow
• Black and white image colorization with OpenCV and Deep Learning
• ML | Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
• ML | Cancer cell classification using Scikit-learn
• ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross Validation
• Human Scream Detection and Analysis for Controlling Crime Rate - Project Idea
• Multiclass image classification using Transfer learning
• Intrusion Detection System Using Machine Learning Algorithms
• Heart Disease Prediction using ANN

Regression Projects

• IPL Score Prediction using Deep Learning
• Dogecoin Price Prediction with Machine Learning
• Zillow Home Value (Zestimate) Prediction in ML
• Calories Burnt Prediction using Machine Learning
• Vehicle Count Prediction From Sensor Data
• Analyzing selling price of used cars using Python
• Box Office Revenue Prediction Using Linear Regression in ML
• House Price Prediction using Machine Learning in Python
• ML | Boston Housing Kaggle Challenge with Linear Regression
• Stock Price Prediction Project using TensorFlow
• Medical Insurance Price Prediction using Machine Learning - Python
• Inventory Demand Forecasting using Machine Learning - Python
• Ola Bike Ride Request Forecast using ML
• Waiter's Tip Prediction using Machine Learning
• Predict Fuel Efficiency Using Tensorflow in Python
• Microsoft Stock Price Prediction with Machine Learning
• Share Price Forecasting Using Facebook Prophet
• Python | Implementation of Movie Recommender System
• How can Tensorflow be used with abalone dataset to build a sequential model?
• OCR of Handwritten digits | OpenCV
• Cartooning an Image using OpenCV - Python
• Count number of Object using Python-OpenCV
• Count number of Faces using Python - OpenCV
• Text Detection and Extraction using OpenCV and OCR
• FaceMask Detection using TensorFlow in Python
• Dog Breed Classification using Transfer Learning
• Flower Recognition Using Convolutional Neural Network
• Emojify using Face Recognition with Machine Learning
• Cat & Dog Classification using Convolutional Neural Network in Python
• Traffic Signs Recognition using CNN and Keras in Python
• Lung Cancer Detection using Convolutional Neural Network (CNN)
• Lung Cancer Detection Using Transfer Learning
• Pneumonia Detection using Deep Learning
• Detecting Covid-19 with Chest X-ray
• Skin Cancer Detection using TensorFlow
• Age Detection using Deep Learning in OpenCV
• Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV
• Detecting COVID-19 From Chest X-Ray Images using CNN
• Image Segmentation Using TensorFlow
• License Plate Recognition with OpenCV and Tesseract OCR
• Detect and Recognize Car License Plate from a video in real time
• Residual Networks (ResNet) - Deep Learning

Natural Language Processing Projects

• Twitter Sentiment Analysis using Python
• Facebook Sentiment Analysis using python
• Next Sentence Prediction using BERT
• Hate Speech Detection using Deep Learning
• Image Caption Generator using Deep Learning on Flickr8K dataset
• Movie recommendation based on emotion in Python
• Speech Recognition in Python using Google Speech API
• Voice Assistant using python
• Human Activity Recognition - Using Deep Learning Model
• Fine-tuning BERT model for Sentiment Analysis
• Sentiment Classification Using BERT
• Sentiment Analysis with an Recurrent Neural Networks (RNN)
• Autocorrector Feature Using NLP In Python
• Python | NLP analysis of Restaurant reviews
• Restaurant Review Analysis Using NLP and SQLite

Clustering Projects

• Customer Segmentation using Unsupervised Machine Learning in Python
• Music Recommendation System Using Machine Learning
• K means Clustering - Introduction
• Image Segmentation using K Means Clustering

Recommender System Project

• AI Driven Snake Game using Deep Q Learning

Machine Learning gained a lot of popularity and become a necessary tool for research purposes as well as for Business. It is a revolutionary field that helps us to make better decisions and automate tasks. Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.

In this article, you’ll find the top 100+ latest Machine Learning projects and Ideas which are beneficial for both beginners and as well experienced professionals. Whether you’re a final-year student aiming for a standout resume or someone building a career, these machine learning projects provide hands-on experience, launching you into the exciting world of Machine Learning and Data Science .

Top Machine Learning Project with Source Code [2024]

We mainly include projects that solve real-world problems to demonstrate how machine learning solves these real-world problems like: – Online Payment Fraud Detection using Machine Learning in Python, Rainfall Prediction using Machine Learning in Python, and Facemask Detection using TensorFlow in Python.

• Machine Learning Project for Beginners
• Advanced Machine Learning Projects

These projects provide a great opportunity for developers to apply their knowledge of machine learning and make an application that benefits society. By implementing these projects in data science, you be familiar with a practical working environment where you follow instructions in real time.

Machine Learning Project for Beginners in 2024 [Source Code]

Let’s look at some of the best new machine-learning projects for beginners in this section and each project deals with a different set of issues, including supervised and unsupervised learning, classification, regression, and clustering. Beginners will be better prepared to tackle more challenging tasks by the time they have finished reading this article and have a better understanding of the fundamentals of machine learning.

1. Healthcare

• ML | Heart Disease Prediction Using Logistic Regression
• Prediction of Wine type using Deep Learning
• Parkinson’s Disease Prediction using Machine Learning in Python
• ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross-Validation

2. Finance and Economics

• Credit Card Fraud Detection

3. Food and Beverage

• Wine Quality Prediction

4. Retail and Commerce

• Sales Forecast Prediction – Python
• IPL Score Prediction Using Deep Learning

6. Health and Fitness

• Medical Insurance Price Prediction using Machine Learning in Python

7. Transportation and Traffic

8. environmental science.

• Rainfall Prediction using Machine Learning in Python

9. Text and Image Processing

• Cartooning an Image using OpenCV – Python
• Count number of Faces using Python – OpenCV

10. Social Media and Sentiment Analysis

11. other important machine learning projects.

• Human Scream Detection and Analysis for Controlling Crime Rate
• Spaceship Titanic Project using Machine Learning in Python
• Inventory Demand Forecasting using Machine Learning in Python
• Waiter’s Tip Prediction using Machine Learning
• Fake News Detection using Machine Learning

Advanced Machine Learning Projects With Source Code [2024]

We have discussed a variety of complex machine-learning ideas in this section that are intended to be challenging for users and span a wide range of topics. These subjects involve creating deep learning models, dealing with unstructured data, and instructing sophisticated models like convolutional neural networks, gated recurrent units, large language models, and reinforcement learning models.

1. Image and Video Processing

• Cat & Dog Classification using Convolutional Neural Network in Python
• Residual Networks (ResNet) – Deep Learning

2. Recommendation Systems

• Ted Talks Recommendation System with Machine Learning

3. Speech and Language Processing

• Fine-tuning the BERT model for Sentiment Analysis
• Sentiment Analysis with Recurrent Neural Networks (RNN)
• Autocorrect Feature Using NLP In Python

4. Health and Medical Applications

5. security and surveillance.

• Detect and Recognize Car License Plate from a video in real-time

6. Gaming and Entertainment

• AI-Driven Snake Game using Deep Q Learning

7. Other Advanced Machine Learning Projects

• Face and Hand Landmarks Detection using Python
• Human Activity Recognition – Using Deep Learning Model
• How can Tensorflow be used with the abalone dataset to build a sequential model?

Machine Learning Projects – FAQs

What are some good machine-learning projects.

For beginners, recommended machine learning projects include sentiment analysis, sales forecast prediction, and image recognition.  

How do I start an ML project?

To start a machine learning project, the first steps involve collecting data, preprocessing it, constructing data models, and then training those models with that data.

Which Language is used for machine learning?

Python and R are most popular and widely-used programming languages for machine learning.

Why do we need to build machine learning projects?

We need to build machine learning projects to solve complex problems, automate tasks and improve decision-making.

What is the future of machine learning?

Machine learning is a fast-growing field of study and research, which means that the demand for machine learning professionals is also growing. 

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Top 9 Machine Learning Project Ideas For All Levels [with Source Code]

Mar 22, 2024 5 Min Read 1027 Views

(Last Updated)

Machine learning is a dynamic field that continues to shape the future of technology and innovation. From predictive analytics to AI-powered chatbots, machine learning projects have the power to solve real-world problems and unlock new potential.

In this article, we will explore 9 machine learning project ideas across different levels of expertise. These projects are carefully curated to cover a wide range of applications, from structured data analysis to image recognition and natural language processing.

By undertaking these projects, you can gain hands-on experience in applying theoretical knowledge to real-world problems, build a robust portfolio, foster problem-solving skills, and continuously expand your understanding of machine learning.

Table of contents

• Why Start a Machine Learning Project?
• How to Start a Machine Learning Project?
• Problem Definition
• Data Collection
• Data Preparation
• Model Selection and Training
• Model Evaluation
• Retrain and Monitor
• Machine Learning Projects for Beginners
• Predict Taxi Fares with Random Forests
• Predicting Credit Card Approvals
• Store Sales Analysis and Forecasting
• Classify Song Genres from Audio Data
• Intermediate Machine Learning Projects
• Find Movie Similarities from Plot Summaries
• Recognising Flower Species with Computer Vision
• Speech Emotion Recognition using Librosa
• Machine Learning Projects for Pros and Our Dear Final-Year Students
• ASL Recognition with Deep Learning
• Stock Market Analysis and Forecasting Using Deep Learning
• Concluding Thoughts...
• What are some interesting machine learning projects?
• Is ML easy to learn?
• Does ML require coding?
• Is ChatGPT just machine learning?

I mean you gotta have a set goal right? So let’s discuss the whys before diving into the actual project deets. Undertaking machine learning projects offers a comprehensive learning experience across diverse domains and technologies.

These projects bridge the gap between theory and practice, allowing you to enhance essential machine-learning skills and build a robust portfolio that showcases your expertise.

Here are some key benefits of starting a machine learning project:

• Practical Experience: Undertaking machine learning projects provides hands-on experience in applying theoretical knowledge to real-world problems. By working on real datasets and implementing machine learning algorithms, you can enhance your understanding of data processing, model evaluation, and other essential skills.
• Portfolio Building: Completing machine learning projects allows you to create a robust portfolio that showcases your skills and knowledge. A well-crafted portfolio can significantly enhance your employability in this competitive field and increase your chances of landing exciting job opportunities.
• Problem Solving: Machine learning projects foster innovative problem-solving and critical thinking skills. By tackling real-world challenges, you can develop a deeper understanding of machine learning functionalities and gain insights into how to approach and solve complex problems.
• Continuous Learning: The diverse nature of machine learning projects promotes exploration and continuous learning within various domains of machine learning. As you work on different projects, you will gain exposure to various techniques, algorithms, and technologies, allowing you to expand your knowledge and skills continuously.

Now that we understand the benefits of starting a machine learning project, let’s learn what exactly are the steps of successfully pulling off a great project!

Make sure you understand machine learning fundamentals like Python, SQL, deep learning, data cleaning, and cloud services before we explore them in the next section. You should consider joining GUVI’s Machine Learning Career Program , which covers tools like Pyspark API, Natural Language Processing, and many more and helps you get hands-on experience by building real-time projects.

Also, if you want to explore Python through a Self-paced course, try GUVI’s Python Self-Paced course.

Must Read: Top 5 Product-Based Companies for Machine Learning Engineers

Starting a machine learning project can be an exciting and rewarding endeavor. While there is no one-size-fits-all approach, here are some general steps to help you get started:

1. Problem Definition

Understand the business problem you want to solve and how machine learning can help. Research existing solutions, open-source projects, and research papers to gain insights into similar applications.

2. Data Collection

Collect relevant data from various sources, making sure it is of high quality and suitable for your problem. Clean and label the data, and create scripts for data validation to ensure its accuracy and consistency.

3. Data Preparation

Prepare the data for analysis by filling in missing values, cleaning and processing the data, and transforming it for machine learning algorithms. Use visualization tools to explore the data and understand its distribution and relationships.

Also Read: Real-World Machine Learning Applications

4. Model Selection and Training

Select suitable machine learning algorithms or deep learning architectures based on your problem and data. Train the models using appropriate techniques such as cross-validation and hyperparameter optimization to achieve optimal results.

5. Model Evaluation

Evaluate the performance of your models using appropriate evaluation metrics. Consider metrics such as accuracy, precision, recall, F1-score, and area under the curve (AUC) depending on your problem. Visualize feature importance to identify the most influential factors in your models.

6. Production

Once you have achieved satisfactory results, it’s time to deploy your machine-learning models into production. Implement MLOps practices such as continuous integration and deployment to ensure smooth and automated deployment processes.

7. Retrain and Monitor

Regularly monitor the performance of your deployed models and retrain them if necessary. Models can degrade over time due to data drift and concept drift. Retraining with new data helps your models adapt to real-time changes and maintain their performance.

Remember, machine learning projects are iterative processes. You may need to revisit and refine each step throughout the project to improve the performance of your models and achieve better results.

Find Out About Machine Learning Vs Deep Learning: A Layman’s Guide to AI in Easy Words

• These beginner machine-learning projects are designed for individuals who are new to machine learning.
• These projects primarily focus on working with structured, tabular data and involve applying data cleaning, processing, and visualization techniques.
• The scikit-learn framework is commonly used to train and validate machine learning models in these projects. If you want to learn the basic concepts of machine learning first, consider taking an introductory course.

1. Predict Taxi Fares with Random Forests

In this project, you will predict the location and time to earn the biggest fare using the New York taxi dataset. You will most likely use the tidy-verse package for data processing and visualization.

To predict location and time, you will experiment with tree-based models such as Decision Trees and Random Forests. You can replicate the results on a different dataset to enhance your problem-solving skills.

Follow along with this GitHub repository to accomplish this one, but do try to put in your changes and tweaks along the way!

2. Predicting Credit Card Approvals

In this project, you will have to build an automatic credit card approval application using hyperparameter optimization and Logistic Regression.

You will handle missing values, process categorical features, perform feature scaling, deal with unbalanced data, and perform automatic hyperparameter optimization using GridCV.

This project will push you out of your comfort zone and help you develop skills in handling complex and real-world datasets.

Also Read : Machine Learning Engineer Resume Guide: 11 Important Things To Include!

3. Store Sales Analysis and Forecasting

In this Kaggle-based project, you will be provided with store sales data and train various time series models to improve your score on the leaderboard.

You will clean the data, perform extensive time series analysis, feature scaling, and train a multivariate time series model. To improve your score, you can use ensembling techniques such as Bagging and Voting Regressors.

You can also explore other datasets, such as the Stock Exchange dataset, to improve your understanding of time series forecasting. Find a similar GitHub project here .

4. Classify Song Genres from Audio Data

In this project, you will use a song dataset to classify songs into two categories: ‘Hip-Hop’ and ‘Rock.’ You will check the correlation between features, normalize the data mostly using Scikit-learn’s StandardScaler, apply Principal Component Analysis (PCA) on scaled data, and visualize the results.

After that, you will train and validate the results using Logistic Regression and Decision Tree models. This guided project will also introduce you to advanced techniques such as class balancing and cross-validation. Find a similar GitHub repo here.

• These intermediate machine-learning projects focus on data processing and training models for structured and unstructured datasets.
• These projects will help you further develop your skills in data cleaning, processing, feature engineering, and statistical analysis.
• Additionally, you will learn how to train models using machine learning frameworks such as sci-kit-learn and perform model evaluation.

5. Find Movie Similarities from Plot Summaries

In this project, you will use Natural Language Processing (NLP) and KMeans clustering to predict the similarity between movies based on their plots from IMDB and Wikipedia (this project is similar to a movie recommendation engine).

You will combine the data, perform tokenization and stemming on text, transform it using TfidfVectorizer, create clusters using the KMeans algorithm, and plot the dendrogram.

You can replicate the results on a different dataset, such as the Netflix Movie dataset, to explore the similarity between movies. Explore this project Find Movie Similarities from Plot Summaries .

Also Read: Best Natural Language Processing (NLP) Books to Read in 2024

6. Recognising Flower Species with Computer Vision

In this project, you will process images and train a system that predicts the label/class of the flower/plant using Computer Vision techniques and Machine Learning algorithms.

You will manipulate and process the images, extract features, use Feature extraction using Deep Convolutional Neural Networks to prepare the data for the model, and then classify the flowers to get the results.

This project will introduce you to image classification and feature extraction techniques using machine learning.

7. Speech Emotion Recognition using Librosa

In this project, you will process sound files using Librosa, sound file, and Scikit-learn to recognize emotions from sound files.

You will load and process sound files, perform feature extraction, and train a Multi-Layer Perceptron (MLP) classifier model.

This project will help you understand the basics of audio processing and introduce you to speech-emotion recognition using machine learning.

• These advanced machine-learning projects focus on building and training deep-learning models and processing unstructured datasets.
• You will work with convolutional neural networks, recurrent neural networks, and other advanced architectures. These projects will help you develop skills in training complex models and working with large-scale datasets.

8. ASL Recognition with Deep Learning

In this project, you will use Keras to build a Convolutional Neural Network (CNN) for American Sign Language (ASL) image classification.

You will visualize the images, analyze the data, process it for modeling, compile, train, and evaluate the CNN on the image dataset.

This project will help you understand image classification using deep learning and introduce you to the ASL recognition domain.

Also Explore: Top 7 Must-Know Machine Learning Tools

9. Stock Market Analysis and Forecasting Using Deep Learning

In this project, you will use Gated Recurrent Units (GRUs) to build deep learning forecasting models for predicting stock prices of companies such as Amazon, IBM, and Microsoft.

You will explore time series analysis, analyze trends and seasonality in stock prices, process the data, and build a GRU model using PyTorch.

This project will introduce you to time series forecasting using deep learning and help you better understand the dynamics of stock markets.

Concluding Thoughts…

Machine learning projects offer a wealth of opportunities for individuals at all levels of expertise. By undertaking machine learning projects, you can enhance your skills in data processing, feature engineering, model training, and deployment.

You will gain valuable experience in working with real-world datasets, implementing cutting-edge algorithms, and solving complex problems.

Also Read: Machine Learning Must-Knows: Reliable Models and Techniques

Kickstart your Machine Learning journey by enrolling in GUVI’s Machine Learning Career Program where you will master technologies like MatplotLib, pandas, SQL, NLP, and deep learning,  and build interesting real-life UI/UX projects.

Alternatively, if you want to explore Python through a Self-paced course, try GUVI’s Python Self-Paced certification course with IIT-M Certification.

Interesting machine-learning projects include image recognition, natural language processing, and recommendation systems. Read the article above to find actual project descriptions in all these fields and many more.

Learning machine learning (ML) can be challenging, but with dedication and resources, it is feasible for individuals with diverse backgrounds. Especially with GUVI’s extensive course resources.

Yes, machine learning often requires coding skills, as programming is essential for implementing algorithms and working with ML frameworks.

Not just but pretty much! ChatGPT is based on machine learning, specifically the GPT-3.5 architecture developed by OpenAI, which uses deep neural networks to generate human-like text.

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About the Author

Jaishree Tomar

A recent CS Graduate with a quirk for writing and coding, a Data Science and Machine Learning enthusiast trying to pave my own way with tech. I have worked as a freelancer with a UK-based Digital Marketing firm writing various tech blogs, articles, and code snippets. Now, working as a Technical Writer at GUVI writing to my heart’s content!

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machine-learning-projects

Here are 172 public repositories matching this topic..., ashishpatel26 / 500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code.

500 AI Machine learning Deep learning Computer vision NLP Projects with code

• Updated Apr 1, 2024

deepfence / FlowMeter

⭐ ⭐ Use ML to classify flows and packets as benign or malicious. ⭐ ⭐

• Updated Apr 8, 2024

qxresearch / qxresearch-event-1

Python hands on tutorial with 50+ Python Application (10 lines of code) @xiaowuc2

• Updated Feb 15, 2024

milaan9 / 93_Python_Data_Analytics_Projects

This repository contains all the data analytics projects that I've worked on in python.

• Updated Dec 9, 2022
• Jupyter Notebook

veeralakrishna / DataCamp-Project-Solutions-Python

DataCamp Project Solutions

• Updated Aug 23, 2023

shsarv / Machine-Learning-Projects

This Contain Some Machine Learning Projects that I have done while understanding ML Concepts.

• Updated Mar 9, 2024

durgeshsamariya / Data-Science-Machine-Learning-Project-with-Source-Code

Data Science and Machine Learning projects with source code.

• Updated Feb 11, 2024

Arshad221b / Sign-Language-Recognition

Indian Sign language Recognition using OpenCV

• Updated Jun 12, 2023

fsiddh / Machine-Learning-Masters

This repository consists content, assignments, assignments solution and study material provided by ineoron ML masters course

• Updated Dec 8, 2022

anubhavshrimal / Machine-Learning

The projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.

• Updated Oct 2, 2020

lawmurray / Birch

A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.

• Updated Sep 21, 2023

inboxpraveen / movie-recommendation-system

Movie Recommendation System with Complete End-to-End Pipeline, Model Intregration & Web Application Hosted. It will also help you build similar projects.

• Updated Dec 13, 2023

ayushreal / Signature-recognition

Signature recognition is a behavioural biometric. It can be operated in two different ways: Static: In this mode, users write their signature on paper, digitize it through an optical scanner or a camera, and the biometric system recognizes the signature analyzing its shape. This group is also known as “off-line”. Dynamic: In this mode, users wri…

• Updated Apr 10, 2018

Viveckh / LilHomie

A Machine Learning Project implemented from scratch which involves web scraping, data engineering, exploratory data analysis and machine learning to predict housing prices in New York Tri-State Area.

vvssttkk / dst

yet another custom data science template via cookiecutter

• Updated Apr 21, 2023

mandliya / ml

A 60 days+ streak of daily learning of ML/DL/Maths concepts through projects

• Updated Jun 30, 2020

souvikmajumder26 / Land-Cover-Semantic-Segmentation-PyTorch

🛣 Building an end-to-end Promptable Semantic Segmentation (Computer Vision) project from training to inferencing a model on LandCover.ai data (Satellite Imagery).

• Updated Jun 20, 2023

thomas-young-2013 / mindware

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

• Updated Jan 26, 2022

FaizanZaheerGit / StudentPerformancePrediction-ML

This is a simple machine learning project using classifiers for predicting factors which affect student grades, using data from CSV file

• Updated Apr 3, 2024

leadthefuture / ML-and-DataScience-preparation

RoadMap and preparation material for Machine Learning and Data Science - From beginner to expert.

• Updated Oct 25, 2022

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