best machine learning projects for resume

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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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.

Explore More  Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

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.

Access Data Science and Machine Learning Project Code Examples

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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100+ Machine Learning Projects with Source Code [2024]

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.

2. Finance and Economics

3. food and beverage, 4. retail and commerce, 6. health and fitness, 7. transportation and traffic, 8. environmental science, 9. text and image processing, 10. social media and sentiment analysis, 11. other important machine learning projects, 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

2. recommendation systems, 3. speech and language processing, 4. health and medical applications, 5. security and surveillance, 6. gaming and entertainment, 7. other advanced machine learning projects, 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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12 Great Machine Learning Projects to Get You Started in AI

Everyone remotely connected to the data world is abuzz about machine learning. Instead of just knowing what machine learning is , why not go one better and start working with it?

If you’re looking to dive into the world of ML but don’t know where to start, I’ve curated 12 hands-on machine learning projects to help kickstart your journey. These projects range from more simple classification tasks to more complex challenges in natural language processing and image recognition. 

Whether you’re a new entrant to data analytics looking for a portfolio project idea, or an intermediate coder interested in machine learning implementations, there’s something here for you. 

While you’re at it, check out our other explainer articles on machine learning models , career paths , and essential machine learning tools . 

We’ll cover:

Classification projects

Natural language processing projects, recommendation projects, computer vision projects, 12 great machine learning projects.

Classification involves sorting data into predefined classes or labels based on their features. Classification projects are a great way for new entrants to machine learning as they help you understand foundational concepts through real-world problems. We’ll take a look at three such machine learning projects.

1. Predicting wine quality

Oenology–or the science of wines, offers a fun introduction to the art of classification. UCI’s wine quality dataset can be used to distinguish between red and white wines. 

You can use libraries such as scikit-learn or Tensorflow to predict the quality of wines based on their 11 physicochemical properties such as acidity, presence of sulfates, and sugar. You’ll learn how to understand feature importance and handle imbalanced datasets through a real-world dataset. While the main task is classification, you can also extend the project to run regression analysis to predict wine quality scores. 

2. Tree species identification

UCI’s leaf dataset is a great way to understand image preprocessing and feature extraction. This project looks at how to best classify tree species based on the shape and texture of their leaves. 

For extra credit, you could augment the original dataset with leaves from your own neighborhood, or extract your own set of features. The learning curve is slightly steeper than most beginner classification projects, but you’ll learn how to create more robust machine learning models .

3. Exoplanet discovery 

For the more extraterrestrially-inclined, Caltech’s Kepler Exoplanet dataset offers a unique classification challenge. Learn how to detect exoplanets (planets outside our solar system) by analyzing light curves observed through NASA’s space telescopes. 

You’ll be challenged to find ways to improve the accuracy of your model through applying advanced time-series analysis on sequential data, and learn how to handle rare events through anomaly detection. While the project requires more intensive domain knowledge research, it’s also an opportunity to demonstrate your ability to dive deep into a new subject area and come up with a useful model. 

Natural language processing stands at the intersection of linguistics and machine learning. It’s a great subject area for coders interested in teaching machines how to understand, interpret, and generate human language in new and useful ways. I’ve listed three compelling NLP projects that are applicable across industries. 

1. Spam detection

Emails would be unusable without spam detection algorithms working in the background to filter out unsolicited messages. UCI’s SMS spam collection dataset contains 5,574 messages labeled as either spam or ham (not spam). 

You can use Python’s Natural Language Toolkit (NLTK) or scikit-learn libraries to learn how to preprocess the text, extract features, and run your prediction model. This is an excellent project to learn how to work with imbalanced datasets–which are common in real-world business scenarios–since spam messages are only a tiny percent of genuine ones. 

2. Sentiment analysis

Every day, millions of people express their opinions online, whether through product reviews or social media posts. 

The sentiment140 dataset , released by a group of Stanford researchers contains 1.6 million tweets for you to train your machine learning model on. It labels tweets as positive, negative, or neutral, which is a relatively easy introduction to the art of sentiment analysis. Learn how to interpret human emotions in text, handle slang, and make decisions on ambiguous or nuanced statements.

You’ll learn how to use techniques such as word embeddings and recurrent neural networks, which feature in many NLP applications.  You can brush up on your skills in our full guide to sentiment analysis .

3. Fake news detector

Google “fake news detection” and you may not be surprised to learn how many active research projects exist to solve this problem. Identifying bias and misinformation is especially difficult given the speed of content creation. 

There are a number of fake news datasets where you can apply popular machine learning libraries like TensorFlow to handle large text data, understand what embeddings are, and grapple with the challenge of creating a model that can reliably understand nuance at scale. 

Recommendation systems, or RecSys in industry speak, power many of the most popular services we use on a day-to-day basis. These systems try to optimize and personalize our experiences based on our preferences and interactions. These machine learning projects are challenging as it can be hard to predict what users like with noisy datasets.

The MovieLens dataset spans 25 million ratings across 62,000 movies. Learn how to use Python libraries such as Surprise or LightFM to build recommender systems with recommendation algorithms. 

You’ll come away with a strong foundation in collaborative filtering, a technique to make recommendations based on user-user or item-item similarities. It’s also a great way to understand concepts like matrix factorization and user embeddings.

2. Building Goodreads

Avid readers will particularly enjoy diving into this project: UCSD’s Goodreads’s dataset is a rich repository of user-book interactions, with more than 2 million books rated by nearly 1 million users.

  Building a reading recommendation system will introduce you to concepts like content-based filtering, where recommendations are based on items’ features and users’ preferences.

3. Restaurant ratings

Yelp’s dataset of almost 7 million restaurant reviews can be used to build a model that recommends restaurants based on reviews and restaurant features. 

Learn how to handle text data, extra features, and understand how to solve thorny “cold start” problems in ML , where your model needs to make accurate recommendations for new users. 

Teaching machines to “see” offers a glimpse into a future where machines can interpret and analyze visual data. If you’re interested in more advanced projects involving the analysis of pixels and patterns, here are three projects with real-world applications. 

1. Facial expressions

Google’s facial expression dataset contains 156,000 images annotated by human raters to classify them according to various emotions. 

You’ll learn how to build a model that can recognise and correctly classify facial expressions using Python libraries such as OpenCV for face detection, and Keras for deep learning. You can take this project in many different directions; one good use case would be to learn how to use convolutional neural networks (CNNs) to enable real-time video processing of human emotions. 

2. Traffic analysis

The promise of self-driving cars has dominated the ML news cycle for a while now. Have you ever wondered about the algorithms powering this? 

If so, check out the University of Albany’s US-DETRAC dataset with 10 hours of traffic video from China. Use Python libraries like Tensorflow’s Object Detection API or YOLO’s state-of-the-art object detection to build an ML system that can detect and analyze vehicles in real-time. While this project will immerse you in challenging concepts like objection detection techniques and tracking algorithms, you’ll come away with an impressive portfolio project at the intersection of computer vision and urban planning. 

3. Human activity 

The University of Central Florida has released an action recognition dataset with more than 13,000 videos across 101 action categories, including knitting, diving, basketball and surfing. 

You’re tasked with building a model that can correctly label these activities, and more, through temporal sequence analysis. You’ll learn how to process video data and 3D convolutional neural networks. This is a great project for more advanced learners looking to understand the specific complexities of identifying human motion through machine learning.

From classifying datasets and deciphering nuanced textual sentiments in NLP, to creating accurate recommendation systems and grappling with the challenges of computer vision, these 12 machine learning projects offer many ways for you to learn new techniques and come away with a strong portfolio project to showcase your skills. 

If the world of data analytics interests you but you don’t know where to start, why not explore our Machine Learning with Python Course ? It covers the basics of machine learning as a field through hands-on projects with an expert mentor, and will give you a good idea of whether or not it’s a career path you’re interested in pursuing further.

You may also be interested in the following articles:

• What Does a Machine Learning Engineer Do?
• Bias in Machine Learning: What Are the Ethics of AI?
• 22 Most-Asked Machine Learning Interview Questions (and Answers!)

How To Write A Machine Learning Project In Resume

• How-To Guides
• Tech Setup & Troubleshooting

Introduction

As the job market becomes increasingly competitive, it is essential to have a resume that stands out from the rest. When it comes to showcasing your skills in the field of machine learning, including relevant projects in your resume can make a significant impact on potential employers.

Machine learning projects demonstrate your practical knowledge and expertise in applying algorithms and models to solve real-world problems. These projects showcase your ability to analyze data, develop innovative solutions, and effectively implement machine learning techniques.

By including machine learning projects in your resume, you provide concrete evidence of your skills and capabilities. Hiring managers often look for candidates who can demonstrate their proficiency through practical applications, and including machine learning projects can help you differentiate yourself from other applicants.

In addition to demonstrating your technical skills, machine learning projects can also highlight your problem-solving abilities, teamwork, and communication skills. These projects usually involve collaboration with other team members, data analysis, experimentation, and presenting findings to stakeholders. Including machine learning projects in your resume gives you the opportunity to showcase your versatility and showcase your ability to work effectively in a team setting.

Machine learning projects can be diverse, ranging from predictive analytics to natural language processing, computer vision, recommendation systems, and more. The key is to choose projects that align with your interests, demonstrate your strengths, and reflect the skills that are relevant to your target job.

In the following sections, we will explore the importance of including machine learning projects on your resume, discuss how to choose the right projects, provide tips on formatting and presenting these projects, and offer sample project descriptions to help you effectively showcase your machine learning expertise on your resume.

Why Should You Include Machine Learning Projects in Your Resume?

In today’s competitive job market, simply listing your academic qualifications and technical skills may not be enough to impress potential employers. Including machine learning projects in your resume can give you a distinct advantage by showcasing your practical experience and highlighting your ability to apply machine learning techniques to real-world problems.

Here are a few reasons why you should consider including machine learning projects in your resume:

• Validation of Skills: Listing machine learning projects on your resume provides tangible evidence of your expertise. It validates the skills you claim to have and shows that you have hands-on experience working with data, algorithms, and models. Employers often value practical experience over theoretical knowledge, and including machine learning projects demonstrates that you can apply your skills effectively.
• Showcasing Problem-Solving Abilities: Machine learning projects require you to identify, analyze, and solve complex problems. By including these projects in your resume, you demonstrate your ability to tackle challenges head-on and develop innovative solutions. It shows employers that you can think critically, work through obstacles, and bring value to their organization.
• Demonstration of Collaboration Skills: Machine learning projects often involve teamwork and collaboration with other professionals, such as data scientists, engineers, and domain experts. Including collaborative projects in your resume highlights your ability to work effectively in a team setting, communicate ideas, and contribute to the success of a project. This can be particularly valuable for employers seeking candidates who can collaborate and function well within their existing teams.
• Highlighting Innovation: Machine learning projects provide an opportunity to showcase your creativity and ability to develop cutting-edge solutions. Employers are often looking for candidates who can bring fresh ideas and approaches to their organization. By including innovative machine learning projects in your resume, you can capture the attention of hiring managers and demonstrate your potential to drive innovation within their company.
• Standing Out from the Competition: In a competitive job market, it is crucial to differentiate yourself from other candidates. Including machine learning projects in your resume sets you apart by demonstrating your practical skills and real-world experience. This can make your resume more memorable and increase your chances of securing an interview or job offer.

By including machine learning projects in your resume, you provide recruiters and hiring managers with concrete examples of your capabilities. It allows them to assess your skills and understand how you can contribute to their organization’s success. So, whether you are a seasoned professional or just starting your career in machine learning, showcasing your projects can make a significant difference in how employers perceive your qualifications and potential.

How to Choose the Right Machine Learning Projects for Your Resume?

When it comes to selecting machine learning projects to include in your resume, it is essential to choose projects that align with your career goals, showcase your skills, and demonstrate your ability to solve real-world problems. Here are some tips to help you choose the right machine learning projects for your resume:

• Relevance: Consider the relevance of the project to the job or industry you’re targeting. Choose projects that align with the specific skills and technologies sought by potential employers. This will show that you have the expertise and experience needed for the role you’re applying for.
• Complexity: Look for projects that demonstrate your ability to handle complex machine learning problems. Employers value candidates who can tackle challenging tasks and find innovative solutions. Choosing projects that showcase your ability to work with large datasets, implement advanced algorithms, or solve unique problems can make a strong impression.
• Diversity: Include a variety of machine learning projects to showcase your versatility. Having projects in different domains or applications can demonstrate your adaptability and ability to apply machine learning techniques to different problem areas. This can be particularly valuable if you are targeting positions in interdisciplinary fields or diverse industries.
• Impact: Consider the impact and outcomes of the projects you have worked on. Highlight projects that have had a tangible impact, such as improved business processes, increased accuracy in predictions, or cost savings. Being able to quantify the results of your projects can make a compelling case for your skills and contributions.
• Collaboration: Include projects that involved collaboration with other team members or stakeholders. This demonstrates your ability to work effectively in a team setting, communicate ideas, and coordinate efforts to achieve project goals. Showcase your role in the project and the specific contributions you made.

When listing machine learning projects on your resume, provide a brief description of each project, highlighting the problem you solved, the methods and techniques you used, the data you worked with, and the results or impact achieved. Be concise yet informative, focusing on the key details that demonstrate your skills and experience.

Remember, the goal is to showcase your expertise and differentiate yourself from other candidates. Choose machine learning projects that not only highlight your technical skills but also reflect your problem-solving abilities, adaptability, and collaboration skills. By selecting the right projects, you can create a compelling resume that catches the attention of potential employers and increases your chances of landing the job you desire.

How to Format and Present Machine Learning Projects on Your Resume?

Once you have chosen the right machine learning projects to include on your resume, it is important to format and present them effectively to grab the attention of potential employers. Here are some tips to help you format and present your machine learning projects on your resume:

• Consistent Structure: Maintain a consistent structure throughout your resume when listing your machine learning projects. Use bullet points or subheadings to clearly separate each project and make them easy to read.
• Project Title: Begin each machine learning project with a clear and concise title that reflects the main focus or objective of the project. This helps employers quickly understand what the project is about.
• Project Description: Provide a brief description of each machine learning project, highlighting the problem you tackled, the techniques and algorithms you used, and the tools or technologies you employed. Emphasize the impact or outcomes of the project, such as improved accuracy, cost savings, or any other measurable results.
• Key Achievements: Include the key achievements or milestones you reached during each project. This could be meeting specific performance metrics, developing a novel solution, or overcoming significant challenges. Highlighting these achievements demonstrates your abilities and shows employers the value you can bring to their organization.
• Data Sources: Mention the sources of data you used for each project. This could include publicly available datasets, proprietary data, or data collected specifically for the project. Including this information gives employers insights into the complexity and relevance of the project.
• Technical Skills: Clearly state the technical skills you utilized in each project. This could include programming languages, libraries or frameworks, data preprocessing techniques, machine learning algorithms, or visualization tools. Highlighting these skills helps employers understand your proficiency in specific technologies and techniques.
• Provide Metrics: Whenever possible, quantify the impact or results of your machine learning project. This could be accuracy percentages, reduction in processing time, cost savings, or other relevant metrics. Providing these metrics adds credibility to your project description and showcases your ability to achieve measurable outcomes.
• Order of Listing: Consider the order in which you list your machine learning projects. If you have multiple projects, prioritize them based on relevance and impact. Place the most relevant and impactful projects at the beginning of the list to capture the attention of employers.

Keep in mind that while it is essential to highlight your machine learning projects, do not overload your resume with too many details. Be concise, focus on the most important aspects of each project, and use bullet points to make the information easily scannable.

By formatting and presenting your machine learning projects effectively, you can ensure that potential employers quickly grasp the value and impact of your work. This will make your resume stand out and increase the likelihood of getting noticed by hiring managers in the competitive job market.

Tips for Effectively Describing Machine Learning Projects on Your Resume

When it comes to describing your machine learning projects on your resume, it is important to effectively communicate your skills, achievements, and the impact of your work to potential employers. Here are some tips to help you describe your machine learning projects in a compelling way:

• Be Concise: Keep your project descriptions concise and to the point. Use clear and specific language to highlight the key aspects of each project without overwhelming the reader with unnecessary details.
• Showcase Achievements: Focus on the achievements and outcomes of your machine learning projects. Mention specific results, improvements, or milestones you achieved during the project. Quantify your achievements whenever possible, such as increased accuracy by a certain percentage or cost savings achieved.
• Highlight Technical Skills: Emphasize the technical skills and tools you utilized in each project. Mention the programming languages, libraries, frameworks, and other technologies you used. This demonstrates your proficiency in these areas and helps employers assess your technical capabilities.
• Explain Project Objectives: Clearly articulate the objectives of each machine learning project. Describe the problem you were trying to solve, the research question being addressed, or the specific goal you aimed to achieve. This provides context to the reader and helps them understand the purpose and relevance of the project.
• Describe the Methodology: Explain the methodology or approach you used in your machine learning projects. Briefly describe the algorithms, models, or techniques you implemented. If you used any innovative or novel methods, highlight them to demonstrate your creative thinking and problem-solving skills.
• Highlight Data Analysis: Emphasize your experience in data analysis and preprocessing. Describe how you cleaned, transformed, and manipulated the data to make it suitable for modeling. Highlight any challenges or unique aspects of the data that you had to address during the project.
• Show Collaboration: If you worked as part of a team during your machine learning projects, highlight your collaboration skills and contributions. Explain how you effectively communicated with team members, collaborated on decision-making, and coordinated efforts to achieve project goals.
• Show Impact and Relevance: Demonstrate the impact and relevance of your machine learning projects. Explain how the project contributed to improved business processes, decision-making, or other relevant outcomes. Connect the project to the industry or field you are applying to, showing its relevance and applicability.
• Tailor Project Descriptions: Customize your project descriptions to align with the requirements and preferences of each potential employer. Research the job description and the company to understand the specific skills and qualities they are seeking. Tailor your project descriptions accordingly to highlight the aspects most relevant to the position.

Remember to proofread your project descriptions for any grammatical errors or formatting inconsistencies. You want your descriptions to be clear, error-free, and well-organized. By effectively describing your machine learning projects, you can effectively convey your skills, accomplishments, and potential value to employers.

Sample Machine Learning Project Descriptions for Resumes

When including machine learning projects on your resume, it is important to provide concise and impactful descriptions that highlight your skills and achievements. Here are some sample machine learning project descriptions to inspire you when showcasing your expertise:

• Predictive Customer Churn Analysis: Developed a predictive model using a Random Forest algorithm to analyze customer churn in a telecommunications company. Leveraged historical customer data, including demographics, usage patterns, and customer support interactions, to predict individuals at risk of churning. Achieved an accuracy of 84% and reduced customer churn by 15% by implementing targeted retention strategies.
• Image Classification for Medical Diagnosis: Implemented a Deep Convolutional Neural Network (CNN) to classify medical images for the early detection of diseases. Trained the model on a dataset of X-ray images and achieved an accuracy of 92% in detecting lung abnormalities. Collaborated with radiologists to fine-tune the model and integrated it into a web application for efficient diagnosis and triage.
• Sentiment Analysis of Social Media Data: Developed a Natural Language Processing (NLP) model to analyze sentiment in Twitter data. Leveraged techniques such as tokenization, word embedding, and LSTM recurrent neural networks to classify tweets as positive, negative, or neutral. Achieved an accuracy of 87% in sentiment classification, enabling businesses to analyze public opinion and make data-driven decisions.
• Recommendation System for E-commerce: Built a collaborative filtering recommendation system for an e-commerce platform. Leveraged user behavior data, including purchase history and user preferences, to provide personalized product recommendations. Improved customer engagement by 20% and increased conversion rates by 10% by suggesting relevant products to users based on their browsing and purchasing patterns.
• Anomaly Detection in Network Traffic: Implemented an unsupervised machine learning model to detect network anomalies. Analyzed network log data and used the Isolation Forest algorithm to identify suspicious activities and potential security breaches. Reduced false positives by 25% and improved detection accuracy by 15% through feature engineering and model optimization.
• Time Series Forecasting for Demand Planning: Developed a time series forecasting model using a Long Short-Term Memory (LSTM) network to predict product demand for a retail company. Trained the model on historical sales data, weather information, and promotional activities to generate accurate demand forecasts. Improved inventory management and reduced stockouts by 30% by optimizing replenishment strategies based on the forecasted demand.
• Automated Fraud Detection in Financial Transactions: Implemented a machine learning model to detect fraudulent transactions in a financial institution. Extracted relevant features from transaction data and used a combination of Random Forest and Gradient Boosting algorithms for classification. Achieved a precision of 95% and reduced false negatives by 20%, resulting in significant cost savings and enhanced security measures.
• Gesture Recognition for Human-Computer Interaction: Designed a gesture recognition system using computer vision techniques to enable gesture-based interaction with electronic devices. Trained a Convolutional Neural Network (CNN) on a dataset of hand gesture images and achieved an accuracy of 92% in recognizing a wide range of gestures. Integrated the system into a smart home environment, allowing users to control devices through intuitive hand movements.

Remember to tailor these project descriptions to reflect your specific contributions, the tools and technologies used, and the impact achieved. By using these sample project descriptions as a framework, you can effectively showcase your machine learning expertise and demonstrate your ability to solve real-world problems.

Incorporating machine learning projects into your resume can greatly enhance your chances of standing out in a competitive job market. These projects allow you to showcase your practical skills, problem-solving abilities, and the impact you can make in the field of machine learning.

By choosing the right projects, formatting them effectively, and providing compelling descriptions, you can effectively highlight your expertise and demonstrate your value to potential employers. Remember to align your projects with your career goals, emphasize relevant achievements, and quantify the results whenever possible. Tailoring your project descriptions to each specific job application will further increase your chances of catching the attention of hiring managers.

Through the sample project descriptions provided, you can gain inspiration and insight into the types of projects that can be included on your resume. Customizing these descriptions to reflect your own contributions and accomplishments will create a more personalized and impactful representation of your machine learning experience.

Don’t forget to proofread your resume thoroughly, ensuring that your machine learning projects are presented clearly and concisely. Avoid jargon and technical terms that may not be familiar to all readers. The goal is to make your projects accessible and compelling to both technical and non-technical individuals.

In conclusion, by effectively showcasing your machine learning projects on your resume, you can differentiate yourself from other candidates, demonstrate your practical skills, and increase your chances of securing the job opportunity you desire. Use your machine learning projects as a powerful tool to highlight your unique abilities and make a lasting impression on potential employers.

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Best Machine Learning Projects With Source Code [2023]

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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Here are 214 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

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qxresearch / qxresearch-event-1

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

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deepfence / FlowMeter

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

• Updated Sep 9, 2024

milaan9 / 93_Python_Data_Analytics_Projects

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

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veeralakrishna / DataCamp-Project-Solutions-Python

DataCamp Project Solutions

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shsarv / Machine-Learning-Projects

This repository showcases a selection of machine learning projects undertaken to understand and master various ML concepts. Each project reflects commitment to applying theoretical knowledge to practical scenarios, demonstrating proficiency in machine learning techniques and tools.

• Updated Sep 14, 2024

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

Data Science and Machine Learning projects with source code.

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Arshad221b / Sign-Language-Recognition

Indian Sign language Recognition using OpenCV

• Updated Jun 12, 2023

data-flair / machine-learning-projects

Machine Learning projects with source code - Machine Learning projects for beginners, ML projects for final year college students, machine learning projects - beginner to advanced

• Updated Aug 15, 2024

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

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

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

lawmurray / Birch

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

• Updated Jun 20, 2024

AI4LAM / awesome-ai4lam

A list of awesome AI in libraries, archives, and museum collections from around the world 🕶️

• Updated Jun 17, 2024

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.

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 May 5, 2024

mandliya / ml

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

• Updated Jun 30, 2020

vvssttkk / dst

yet another custom data science template via cookiecutter

• Updated Apr 21, 2023

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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 the 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 explores 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!

Blog Bonus: Learn how to showcase your project work in your resume - Download Now

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 attributes, 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 the 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 help 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

Prerequisite knowledge required 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. Difficulty level to expect 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. Time needed 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 the end of the project, you would have a deeper understanding of analyzing 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

Prerequisite knowledge required 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. Difficulty level to expect 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 intermediate Python developers and Machine Learning professionals. Time needed 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 datasets. Not to forget, Python's libraries are much more practical.

By the 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. 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 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

Prerequisite knowledge required 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. Difficulty level to expect 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 a final year project. Time needed 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 the 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:

Prerequisite knowledge required 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. Difficulty level to expect 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. Time needed 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 CrioDo

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Top 10 Machine Learning Projects to Boost Your Resume

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

The AI and machine learning industries are booming like never before. As of 2021, the increase in AI usage across businesses will create US$2.9 trillion in business value. AI has automated many industries across the globe and changed the way they operate. Most large companies incorporate AI to maximize productivity in their workflow, and industries like marketing and healthcare have undergone a paradigm shift due to the consolidation of AI.

Sales Forecasting

Time-series forecasting is a machine learning technique used very often in the industry. The use of past data to predict future sales has a large number of business use cases. The Kaggle Demand Forecasting dataset can be used to practice this project. This dataset has 5 years of sales data, and you will need to predict sales for the next three months. There are ten different stores listed in the dataset, and there are 50 items at each store. To predict sales, you can try out various methods — ARIMA, vector autoregression, or deep learning. One method you can use for this project is to measure the increase in sales for each month and record it. Then, build the model on the difference between the previous month and the present month sales. Taking into account factors like holidays and seasonality can improve the performance of your machine learning model.

Customer Service Chatbot

A customer service chatbot uses AI and machine learning techniques to reply to customers, taking the role of a human representative. A chatbot should be able to answer simple questions to satisfy customer needs.

There are presently three kinds of chatbots that you can build:

• 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.

An NLP chatbot is an interesting machine learning project idea. You will need an existing corpus of words to train your model on, and you can easily find Python libraries to do this. You can also have a pre-defined dictionary with a list of question-and-answer pairs you'd like to train your model.

Wildlife Object Detection System

If you live in an area with frequent wild-animal sightings, it is helpful to implement an object detection system to identify their presence in your area. Follow these steps to build a system like this:

• 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.

Microsoft has built an Image Recognition API using data collected from wildlife cameras. They released an open-source pre-trained model for this purpose called a MegaDetector. You can use this pre-trained model in your Python application to identify wild animals from the images collected. It is one of the most exciting ML projects mentioned so far and is pretty simple to implement due to the availability of a pre-trained model for this purpose.

Spotify Music Recommender System

Spotify uses AI to recommend music to its users. You can try building a recommender system based on publicly available data on Spotify. Spotify has an API that you can use to retrieve audio data — you can find features like the year of release, key, popularity, and artist. To access this API in Python, you can use a library called Spotify. You can also use the Spotify dataset on Kaggle that has around 600K rows. Using these datasets, you can suggest the best alternative to each user's favorite musician. You can also come up with song recommendations based on the content and genre preferred by each user. This recommender system can be built using K-Means clustering — similar data points will be grouped. You can recommend songs with a minimal intra-cluster distance between them to the end-user. Once you have built the recommender system, you can also turn it into a simple Python app and deploy it. You can get users to enter their favorite songs on Spotify, then display your model recommendations on the screen that have the highest similarity to the songs they enjoyed.

Market Basket Analysis

Market Basket Analysis can help companies identify hidden correlations between items that are frequently bought together. These stores can then position their items in a way that allows people to find them easier. You can use the Market Basket optimization dataset on Kaggle to build and train your model. The most commonly used algorithm used to perform Market Basket analysis is the Apriori algorithm.

NYC Taxi Trip Duration

The dataset has variables that include start and end coordinates of a taxi trip, time, and the number of passengers. The goal of this ML project is to predict trip duration with all these variables. It is a regression problem. Variables like time and coordinates need to be pre-processed appropriately and converted into an understandable format. This project isn't as straightforward as it seems. This dataset also has some outliers that make prediction more complex, so you will need to handle this with feature engineering techniques. The evaluation criteria for this NYC Taxi Trip Kaggle Competition is RMSLE or the Root Mean Squared Log Error. The top submission on Kaggle received an RMSLE score of 0.29, and Kaggle's baseline model has an RMSLE of 0.89. You can use any regression algorithm to solve this Kaggle project, but the highest performing competitors of this challenge have either used gradient boosting models or deep learning techniques.

Real-Time Spam Detection

In this project, you can use machine learning techniques to distinguish between spam (illegitimate) and ham (legitimate) messages. To achieve this, you can use the Kaggle SMS spam collection dataset. This dataset contains a set of approximately 5K messages that have been labeled as spam or ham. To build the machine learning model, you first need to pre-process the text messages present in Kaggle's SMS spam collection dataset. Then, convert these messages into a bag of words so that they can easily be passed into your classification model for prediction.

Myers-Briggs Personality Prediction App

You can create an app to predict a user's personality type based on what they say. The Myers-Briggs type indicator categorizes individuals into 16 different personality types. It is one of the most popular personality tests in the world. If you try to find your personality type on the Internet, you will find many online quizzes. After answering around 20–30 questions, you will be assigned to a personality type. However, in this project, you can use machine learning to predict anyone's personality type just based on one sentence.

Here are the steps you can take to achieve this:

• 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

You can build an app that recognizes a user's mood based on live web footage and a movie suggestion based on the user's expression.

To build this, you can take the following steps:

• 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

In this project, you can create a dashboard analyzing the overall sentiment of popular YouTubers. Over 2 billion users watch YouTube videos at least once a month. Popular YouTubers garner hundreds of billions of views with their content. However, many of these influencers have come under fire due to controversies in the past, and public perception is constantly changing. You can build a sentiment analysis model and create a dashboard to visualize sentiments around celebrities over time.

• 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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