machine learning problem solving process

How to Approach Machine Learning Problems

How do you approach machine learning problems? Are neural networks the answer to nearly every challenge you may encounter?

In this article, Toptal Freelance Python Developer Peter Hussami explains the basic approach to machine learning problems and points out where neural may fall short.

By Peter Hussami

Peter’s rare math-modeling expertise includes audio and sensor analysis, ID verification, NPL, scheduling, routing, and credit scoring.

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One of the main tasks of computers is to automate human tasks. Some of these tasks are simple and repetitive, such as “move X from A to B.” It gets far more interesting when the computer has to make decisions about problems that are far more difficult to formalize. That is where we start to encounter basic machine learning problems.

Historically, such algorithms were built by scientists or experts that had intimate knowledge of their field and were largely based on rules. With the explosion of computing power and the availability of large and diverse data sets, the focus has shifted toward a more computational approach.

Most popularized machine learning concepts these days have to do with neural networks, and in my experience, this created the impression in many people that neural networks are some kind of a miracle weapon for all inference problems. Actually, this is quite far from the truth. In the eyes of the statistician, they form one class of inference approaches with their associated strengths and weaknesses, and it completely depends on the problem whether neural networks are going to be the best solution or not.

Quite often, there are better approaches.

In this article, we will outline a structure for attacking machine learning problems. There is no scope for going into too much detail about specific machine learning models , but if this article generates interest, subsequent articles could offer detailed solutions for some interesting machine learning problems.

First, however, let us spend some effort showing why you should be more circumspect than to automatically think “ neural network ” when faced with a machine learning problem.

Pros and Cons of Neural Networks

With neural networks, the inference is done through a weighted “network.” The weights are calibrated during the so-called “learning” process, and then, subsequently, applied to assign outcomes to inputs.

As simple as this may sound, all the weights are parameters to the calibrated network, and usually, that means too many parameters for a human to make sense of.

So we might as well just consider neural networks as some kind of an inference black box that connects the input to output, with no specific model in between.

Let us take a closer look at the pros and cons of this approach.

Advantages of Neural Networks

• The input is the data itself. Usable results even with little or no feature engineering.
• Trainable skill. With no feature engineering, there is no need for such hard-to-develop skills as intuition or domain expertise. Standard tools are available for generic inferences.
• Accuracy improves with the quantity of data. The more inputs it sees, the better a neural network performs.
• May outperform classical models when there is not full information about the model. Think of public sentiment, for one.
• Open-ended inference can discover unknown patterns. If you use a model and leave a consideration out of it, it will not detect the corresponding phenomenon. Neural networks might.

Successful neural network example: Google’s AI found a planet orbiting a distant star—where NASA did not—by analyzing accumulated telescope data.

Disadvantages of Neural Networks

• They require a lot of (annotated!) data. First, this amount of data is not always available. Convergence is slow. A solid model (say, in physics) can be calibrated after a few observations—with neural networks, this is out of the question. Annotation is a lot of work, not to mention that it, in itself, is not foolproof.
• No information about the inner structure of the data. Are you interested in what the inference is based on? No luck here. There are situations where manually adjusting the data improves inference by a leap, but a neural network will not be able to help with that.
• Overfitting issues. It happens often that the network has more parameters than the data justifies, which leads to suboptimal inference.
• Performance depends on information. If there is full information about a problem, a solid model tends to outperform a neural network.
• There are sampling problems. Sampling is always a delicate issue, but with a model, one can quickly develop a notion of problematic sampling. Neural networks learn only from the data, so if they get biased data, they will have biased conclusions.

An example of failure: A personal relation told me of a major corporation (that I cannot name) that was working on detecting military vehicles on aerial photos. They had images where there were such vehicles and others that did not. Most images of the former class were taken on a rainy day, while the latter were taken in sunny weather. As a result, the system learned to distinguish light from shadow.

To sum up, neural networks form one class of inference methods that have their pros and cons.

The fact that their popularity outshines all other statistical methods in the eyes of the public has likely more to do with corporate governance than anything else.

Training people to use standard tools and standardized neural network methods is a far more predictable process than hunting for domain experts and artists from various fields. This, however, does not change the fact that using a neural network for a simple, well-defined problem is really just shooting a sparrow with a cannon: It needs a lot of data, requires a lot of annotation work, and in return might just underperform when compared to a solid model. Not the best package.

Still, there is huge power in the fact that they “democratize” statistical knowledge. Once a neural network-based inference solution is viewed as a mere programming tool, it may help even those who don’t feel comfortable with complex algorithms. So, inevitably, a lot of things are now built that would otherwise not exist if we could only operate with sophisticated models.

Approaching Machine Learning Problems

When approaching machine learning problems, these are the steps you will need to go through:

• Setting acceptance criteria
• Cleaning your data and maximizing ist information content
• Choosing the most optimal inference approach
• Train, test, repeat

Let us see these items in detail.

Setting Acceptance Criteria

You should have an idea of your target accuracy as soon as possible, to the extent possible. This is going to be the target you work towards.

Cleansing Your Data and Maximizing Its Information Content

This is the most critical step. First of all, your data should have no (or few) errors. Cleansing it of these is an essential first step. Substitute missing values, try to identify patterns that are obviously bogus, eliminate duplicates and any other anomaly you might notice.

As for information, if your data is very informative (in the linear sense), then practically any inference method will give you good results. If the required information is not in there, then the result will be noise. Maximizing the information means primarily finding any useful non-linear relationships in the data and linearizing them. If that improves the inputs significantly, great. If not, then more variables might need to be added. If all of this does not yield fruit, target accuracy may suffer.

With some luck, there will be single variables that are useful. You can identify useful variables if you—for instance—plot them against the learning target variable(s) and find the plot to be function-like (i.e., narrow range in the input corresponds to narrow range in the output). This variable can then be linearized—for example, if it plots as a parabola, subtract some values and take the square root.

For variables that are noisy—narrow range in input corresponds to a broad range in the output—we may try combining them with other variables.

To have an idea of the accuracy, you may want to measure conditional class probabilities for each of your variables (for classification problems) or to apply some very simple form of regression, such as linear regression (for prediction problems). If the information content of the input improves, then so will your inference, and you simply don’t want to waste too much time at this stage calibrating a model when the data is not yet ready. So keep testing as simple as possible.

Choosing the Most Optimal Inference Approach

Once your data is in decent shape, you can go for the inference method (the data might still be polished later, if necessary).

Should you use a model? Well, if you have good reason to believe that you can build a good model for the task, then you probably should. If you don’t think so, but there is ample data with good annotations, then you may go hands-free with a neural network. In practical machine learning applications, however, there is often not enough data for that.

Playing accuracy vs. cover often pays off tremendously. Hybrid approaches are usually completely fine. Suppose the data is such that you can get near-100% accuracy on 80% of it with a simple model? This means you can demonstrable results quickly, and if your system can identify when it’s operating on the 80% friendly territory, then you’ve basically covered most of the problem. Your client may not yet be fully happy, but this will earn you their trust quickly. And there is nothing to prevent you from doing something similar on the remaining data: with reasonable effort now you cover, say, 92% of the data with 97% accuracy. True, on the rest of the data, it’s a coin flip, but you already produced something useful.

For most practical applications, this is very useful. Say, you’re in the lending business and want to decide whom to give a loan, and all you know is that on 70% of the clients your algorithm is very accurate. Great—true, the other 30% of your applicants will require more processing, but 70% can be fully automated. Or: suppose you’re trying to automate operator work for call centers, you can do a good (quick and dirty) job on the most simple tasks only, but these tasks cover 50% of the calls? Great, the call center saves money if they can automate 50% of their calls reliably.

To sum up: If the data is not informative enough, or the problem is too complex to handle in its entirety, think outside the box. Identify useful and easy-to-solve sub-problems until you have a better idea.

Once you have your system ready, learn, test and loop it until you’re happy with the results.

Train, Test, Repeat

After the previous steps, little of interest is left. You have the data, you have the machine learning method, so it’s time to extract parameters via learning and then test the inference on the test set. Literature suggests 70% of the records should be used for training, and 30% for testing.

If you’re happy with the results, the task is finished. But, more likely, you developed some new ideas during the procedure, and these could help you notch up in accuracy. Perhaps you need more data? Or just more data cleansing? Or another model? Either way, chances are you’ll be busy for quite a while.

So, good luck and enjoy the work ahead!

Further Reading on the Toptal Blog:

• Identifying the Unknown With Clustering Metrics
• Advantages of AI: Using GPT and Diffusion Models for Image Generation
• Stars Realigned: Improving the IMDb Rating System
• Machines and Trust: How to Mitigate AI Bias

Understanding the basics

Machine learning vs. deep learning: what's the difference.

Machine learning includes all inference techniques while deep learning aims at uncovering meaningful non-linear relationships in the data. So deep learning is a subset of machine learning and also a means of automated feature engineering applied to a machine learning problem.

Which language is best for machine learning?

The ideal choice is a language that has both broad programming library support and allows you to focus on the math rather than infrastructure. The most popular language is Python, but algorithmic languages such as Matlab or R or mainstreamers like C++ and Java are all valid choices as well.

Machine learning vs. neural networks: What's the difference?

Neural networks represent just one approach within machine learning with its pros and cons as detailed above.

What is the best way to learn machine learning?

There are some good online courses and summary pages. It all depends on one’s skills and tastes. My personal advice: Think of machine learning as statistical programming. Beef up on your math and avoid all sources that equate machine learning with neural networks.

What are the advantages and disadvantages of artificial neural networks?

Some advantages: no math, feature engineering, or artisan skills required; easy to train; may uncover aspects of the problem not originally considered. Some disadvantages: requires relatively more data; tedious preparation work; leaves no explanation as to why they decide the way they do, overfitting.

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Peter Hussami

Budapest, Hungary

Member since July 3, 2017

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Solve Problems With Machine Learning Effectively in Four Steps

The projected $15.7 trillion contribution of Artificial Intelligence by 2030 highlights its transformative potential, driving widespread adoption across sectors. As we approach this revolution, we will need to figure out which tasks can be effectively tackled with Machine Learning (ML). So, how do you effectively solve problems with Machine Learning (ML)?

Effective problem-solving with Machine Learning boils down to four key factors:

• Environment

Solve Problems with Machine Learning: Yay or Nay?

In today’s business landscape, data reigns supreme, but do we really need AI to navigate its complexities? While it may seem appealing to tout AI-powered solutions, the reality is often more nuanced. Thus, there’s a growing need to educate ourselves on the problems that AI and machine learning (ML) can effectively solve.

Google suggests focusing on problem understanding before diving into AI development, ensuring that solutions are tailored to real-world challenges. Rather than rushing to architect algorithms and make assumptions about data attributes and volume, it’s essential to dedicate ample time to understanding the problem statement thoroughly.

Step One: Identify Patterns to Solve Problems with Machine Learning

The concept of a pattern revolves around using AI to analyze data effectively. Once you thoroughly understand the problem, you start exploring potential solutions. This involves testing different versions of the model to uncover weaknesses and refine it further.

Documenting your progress is crucial, as it helps capture both successful outcomes and lessons learned from failures. This accumulated knowledge guides your intuition as a data scientist when identifying solvable problems for ML.

Another key aspect is data availability. Without data, even the most advanced models are useless. If necessary, you can use publicly available data or simulate data for your business case. However, the model’s predictive power relies heavily on having relevant attributes with significant signals.

The quantity of data needed depends on the complexity of the ML task. Machine Learning’s effectiveness to solve a problem correlates directly with data quality, so it’s essential to include only relevant features and eliminate noise.

Choosing the best model from numerous hypotheses and experiments can be challenging. Ideally, the best model generalizes well to unseen data. Creating an evaluation dataset that mirrors real-world scenarios is crucial for assessing the model’s performance in production. Running the best model on this dataset helps determine its true efficacy.

Step 2: Building Machine Learning Models for Environments

What is the environment? Simply put, it is the setting for which is Machine Learning Model is built. Developing a machine learning model for dynamic environments is tricky. A dymanic environment where there are multiple variables. It’s crucial to set realistic expectations for the model.

Creating models for such dynamic environments requires deep knowledge of the field. It’s not just about fancy algorithms but also understanding the intricacies of the environment in question. So, effective ML models here need both technical skills and nsights.

Let us look at this with an example.

Problem-Solving with Machine Learning: Predicting Stock Exchange Prices

• There will be constantly changing factors, both micro and macro-economic. Stock prices fluctuate due to various factors like market sentiment, economic indicators, and news events.
• Realistic expectations involve comparing model performance to what an average person with basic stock market knowledge could achieve, rely on basic patterns to make decisions.
• An unrealistic expectation would be to expect the model to give results akin to a seasoned expert who has learned nuances and intuitions through repeated trial and error.

Did you know there are many Open Source ML environments available for you to get started?

Step 3: Validate the Machine Learning Model

Before beginning the modeling process, it’s crucial to understand how the model’s performance relates to business objectives. Scientific metrics like Precision, Recall, RMSE, and ROC-AUC are commonly used to assess model performance, but it’s essential to consider their impact on business outcomes.

For example, achieving an 80% Precision score may seem good, but how does it affect conversion rates and revenue?Validation of model predictions is important. This involves establishing a consensus on how human experts will evaluate the model’s output.

In essence, it’s about ensuring that the model’s performance metrics align with real-world business goals and that its predictions are evaluated in a meaningful way by experts.

Step Four: Consider the Dimensions

The fourth factor you need to consider before applying AI and ML to problem-solving is the dimensions. As more factors are considered, the estimate becomes more accurate but performing calculations manually becomes challenging. Machine learning excels in mining data associations on large datasets, providing more accurate predictions compared to human experts, making it easier for you to solve problems.

Let’s also consider this with an example.

Problem-Solving with Machine Learning: Predicting Housing Market Trends

Estimating house market value involves considering factors like area, number of rooms, and age of the house. Initially, correlating price with area gives a rough estimate but may not be accurate. Considering additional factors like number of rooms, age of the house, proximity to amenities like schools and medical facilities improves accuracy.

Would you like to start learning how Artificial Intelligence and Machine Learning work? If yes, don’t leave without taking a look at the courses we offer!

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Introduction to Machine Learning Problem Framing

Introduction to Machine Learning Problem Framing teaches you how to determine if machine learning (ML) is a good approach for a problem and explains how to outline an ML solution.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2022-07-18 UTC.

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What is Machine Learning? A Comprehensive Guide for Beginners

• Written by Karin Kelley
• Updated on February 12, 2024

In our increasingly digitized world, machine learning (ML) has gained significant prominence. From self-driving cars to personalized recommendations on streaming platforms, ML algorithms are revolutionizing various aspects of our lives.

But what is machine learning exactly? This blog will unravel the mysteries behind this transformative technology, shedding light on its inner workings and exploring its vast potential. We’ll also share how you can learn machine learning in an online ML course .

What is Machine Learning, and How Does it Work?

At its core, machine learning is a branch of artificial intelligence (AI) that equips computer systems to learn and improve from experience without explicit programming. In other words, instead of relying on precise instructions, these systems autonomously analyze and interpret data to identify patterns, make predictions, and make informed decisions.

The key to the power of ML lies in its ability to process vast amounts of data with remarkable speed and accuracy. By feeding algorithms with massive data sets, machines can uncover complex patterns and generate valuable insights that inform decision-making processes across diverse industries, from healthcare and finance to marketing and transportation.

Also Read: AI ML Engineer Salary – What You Can Expect

History of Machine Learning: Pioneering the Path to Intelligent Automation

Machine learning, as we know it today, results from decades of groundbreaking research, technological advancements, and visionary minds. Let’s take a journey through time to explore the key milestones and notable events that have shaped the history of ML:

• 1943: Warren McCulloch and Walter Pitts laid the foundation for artificial neural networks, proposing a mathematical model of how neurons in the brain could compute and learn.
• 1950: Alan Turing introduces the concept of the “imitation game,” which became the Turing test, which aims to determine a machine’s ability to exhibit intelligent behavior indistinguishable from a human’s.
• 1956: The Dartmouth Workshop, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, marks the birth of artificial intelligence as a formal research field and sets the stage for future advancements in ML.
• 1957: Frank Rosenblatt developed the perceptron, an early form of an artificial neural network capable of learning and making decisions based on inputs.
• 1967: The “nearest neighbor” algorithm, introduced by Peter Hart, Richard Duda, and David Stork, paves the way for pattern recognition and classification tasks in machine learning.
• 1979: The backpropagation algorithm, proposed by Paul Werbos, revolutionizes the training of artificial neural networks, enabling them to learn from data through iterative weight adjustments.
• 1986: The concept of “deep learning” emerges as Geoffrey Hinton, along with David Rumelhart and Ronald Williams, demonstrates the successful training of multi-layered neural networks, unlocking their potential for complex pattern recognition tasks.
• 1997: IBM’s Deep Blue defeats world chess champion Garry Kasparov, showcasing the power of ML in strategic decision-making and inspiring further advancements in game-playing algorithms.
• 2006: Jeff Dean and Andrew Ng release Google’s groundbreaking machine learning library, TensorFlow, providing researchers and developers with a powerful toolset for building and deploying ML models.
• 2011: IBM’s Watson wins the game show Jeopardy!, marking a significant milestone in natural language processing and demonstrating the capability of machine learning algorithms to understand and respond to human language.
• 2012: AlexNet, a deep convolutional neural network developed by Alex Krizhevsky, wins the ImageNet Large Scale Visual Recognition Challenge, propelling the resurgence of deep learning and its applications in computer vision.
• 2014: Facebook introduces DeepFace, a facial recognition system powered by deep learning, achieving unprecedented accuracy in identifying faces across vast data sets.
• 2016: AlphaGo, developed by DeepMind, defeats world champion Go player Lee Sedol, showcasing the prowess of ML algorithms in mastering complex strategic games.
• 2017: Generative adversarial networks (GANs), introduced by Ian Goodfellow, revolutionize the field of generative modeling, enabling the creation of realistic synthetic data and driving advancements in computer vision and creative applications.
• 2018: OpenAI introduces GPT (Generative Pre-trained Transformer), a language model capable of generating coherent and contextually relevant text, pushing the boundaries of natural language processing.
• 2020: The COVID-19 pandemic sparks numerous machine learning initiatives, ranging from vaccine development and drug discovery to epidemiological forecasting and contact tracing, highlighting the invaluable role of ML in addressing global challenges.

The history of machine learning is a testament to human ingenuity, perseverance, and the continuous pursuit of pushing the boundaries of what machines can achieve. Today, ML is integrated into various aspects of our lives, propelling advancements in healthcare, finance, transportation, and many other fields, while constantly evolving.

What is Machine Learning, and Why Do We Need It?

The need for machine learning has become more apparent in our increasingly complex and data-driven world. Traditional approaches to problem-solving and decision-making often fall short when confronted with massive amounts of data and intricate patterns that human minds struggle to comprehend. With its ability to process vast amounts of information and uncover hidden insights, ML is the key to unlocking the full potential of this data-rich era.

First and foremost, machine learning enables us to make more accurate predictions and informed decisions. ML algorithms can provide valuable insights and forecasts across various domains by analyzing historical data and identifying underlying patterns and trends. From weather prediction and financial market analysis to disease diagnosis and customer behavior forecasting, the predictive power of machine learning empowers us to anticipate outcomes, mitigate risks, and optimize strategies.

Moreover, it can potentially transform industries and improve operational efficiency. With its ability to automate complex tasks and handle repetitive processes, ML frees up human resources and allows them to focus on higher-level activities that require creativity, critical thinking, and problem-solving. ML offers unprecedented opportunities for organizations to increase productivity and streamline operations, from streamlining supply chain management and optimizing logistics routes to automating quality control and enhancing customer support through chatbots.

In summary, the need for ML stems from the inherent challenges posed by the abundance of data and the complexity of modern problems. By harnessing the power of machine learning, we can unlock hidden insights, make accurate predictions, and revolutionize industries, ultimately shaping a future that is driven by intelligent automation and data-driven decision-making.

Also Read: What are Today’s Top Ten AI Technologies?

What are the Applications of Machine Learning?

The applications of machine learning are virtually limitless. Machine-learning algorithms are woven into the fabric of our daily lives, from spam filters that protect our inboxes to virtual assistants that recognize our voices. They enable personalized product recommendations, power fraud detection systems, optimize supply chain management, and drive advancements in medical research, among countless other endeavors.

What are the Main Types of ML?

Let’s start diving deeper into our answer to “What is machine learning?”

ML algorithms can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning. In supervised machine learning, algorithms are trained on labeled data sets, enabling them to make predictions or classify new, unseen data accurately. On the other hand, unsupervised machine learning involves training algorithms on unlabeled data, enabling them to identify hidden patterns and structures within the information. Lastly, reinforcement learning involves training algorithms to make a series of decisions based on feedback received from the environment, aiming to maximize a specific reward.

What Are the Main Algorithms Used in ML?

Machine learning encompasses various algorithms designed to tackle specific tasks and data types. Here are some of the main algorithms commonly used in ML:

• Linear Regression: This algorithm predicts a continuous output variable based on one or more inputs, assuming a linear relationship between them.
• Logistic Regression: Logistic regression is used for binary classification tasks, predicting the probability of an event belonging to one of two classes based on input features.
• Decision Trees: Decision trees are versatile algorithms for classification and regression tasks. They create a flowchart-like structure based on data features, enabling decision-making based on learned patterns.
• Random Forest: A random forest is an ensemble learning method that combines multiple decision trees to improve accuracy and reduce overfitting. It is effective for both classification and regression tasks.
• Support Vector Machines (SVM): SVM is a powerful algorithm for classification and regression tasks. It identifies a hyperplane that maximally separates data points of different classes or predicts continuous values.
• Naive Bayes: Naive Bayes is a probabilistic algorithm commonly used for classification tasks. It applies Bayes’ theorem with the assumption of feature independence to make predictions.
• K-Nearest Neighbors (KNN): KNN is a non-parametric algorithm for classification and regression tasks. It predicts based on the similarity of new instances to its k nearest neighbors in the training data.
• Neural Networks: Neural networks are versatile algorithms inspired by the human brain’s structure. They consist of interconnected nodes (neurons) organized in layers, enabling them to learn complex patterns and solve various tasks like classification, regression, and image recognition.
• Clustering Algorithms: Clustering algorithms, such as K-means and DBSCAN, group similar data points together based on their characteristics, identifying hidden structures or patterns within unlabeled data.
• Reinforcement Learning: Reinforcement learning is an algorithmic approach where an agent learns to make sequential decisions based on feedback from the environment, aiming to maximize a reward signal.

These are just a few examples of the algorithms used in machine learning. Depending on the problem, different algorithms or combinations may be more suitable, showcasing the versatility and adaptability of ML techniques.

Comparing Machine Learning vs. Deep Learning vs. Neural Networks

Machine learning, deep learning, and neural networks are all interconnected terms that are often used interchangeably, but they represent distinct concepts within the field of artificial intelligence. Let’s explore the key differences and relationships between these three concepts.

Machine Learning

Machine learning is a broad umbrella term encompassing various algorithms and techniques that enable computer systems to learn and improve from data without explicit programming. It focuses on developing models that can automatically analyze and interpret data, identify patterns, and make predictions or decisions. ML algorithms can be categorized into supervised machine learning, unsupervised machine learning, and reinforcement learning, each with its own approach to learning from data.

Neural Networks

Neural networks are a subset of ML algorithms inspired by the structure and functioning of the human brain. They consist of interconnected nodes (neurons) organized in layers. Each neuron processes input data, applies a mathematical transformation, and passes the output to the next layer. Neural networks learn by adjusting the weights and biases between neurons during training, allowing them to recognize complex patterns and relationships within data. Neural networks can be shallow (few layers) or deep (many layers), with deep neural networks often called deep learning.

Deep Learning

Deep learning is a subfield of machine learning that focuses on training deep neural networks with multiple layers. It leverages the power of these complex architectures to automatically learn hierarchical representations of data, extracting increasingly abstract features at each layer. Deep learning has gained prominence recently due to its remarkable success in tasks such as image and speech recognition, natural language processing, and generative modeling. It relies on large amounts of labeled data and significant computational resources for training but has demonstrated unprecedented capabilities in solving complex problems.

In summary, machine learning is the broader concept encompassing various algorithms and techniques for learning from data. Neural networks are a specific type of ML algorithm inspired by the brain’s structure. Conversely, deep learning is a subfield of ML that focuses on training deep neural networks with many layers. Deep learning is a powerful tool for solving complex tasks, pushing the boundaries of what is possible with machine learning.

Also Read: The Future of AI: A Comprehensive Guide

What are the Advantages and Disadvantages of ML?

Advantages of machine learning.

• Increased Accuracy: ML algorithms can process and analyze vast amounts of data, leading to more accurate predictions and decision-making than traditional methods.
• Time and Cost Efficiency: Automating tasks and processes can significantly reduce time and costs associated with manual labor, leading to improved efficiency and resource allocation.
• Scalability: ML models can handle large and complex data sets, allowing for scalability and adaptability to changing business needs.
• Real-Time Insights: Machine learning algorithms can analyze data in real time, enabling organizations to respond promptly to emerging trends, anomalies, or threats.
• Pattern Recognition: ML algorithms excel at identifying complex patterns and relationships within data, leading to valuable insights and improved understanding of various phenomena.

Disadvantages of Machine Learning

• Data Dependency: ML algorithms rely heavily on data quality and quantity for training. Insufficient or biased data can lead to inaccurate or biased outcomes.
• Overfitting or Underfitting: ML models can overfit or underfit the training data, resulting in poor generalization to new data. Proper model tuning and validation techniques are required to mitigate this issue.
• Lack of Interpretability: Some machine learning algorithms, such as deep neural networks, operate as black boxes, making it challenging to interpret and explain their decision-making process.
• Ethical Concerns: Machine learning systems can perpetuate biases present in the data they are trained on, leading to discriminatory outcomes. Ensuring fairness and addressing ethical considerations in algorithm design is crucial.
• Initial Investment and Expertise: Implementing ML solutions often requires significant investment in computational resources, infrastructure, and skilled personnel for development, training, and maintenance.

It is important to note that while ML offers numerous advantages, careful consideration of its limitations and ethical implications is essential for responsible and effective deployment.

Why Learn Machine Learning and How to Get Started

ML has become indispensable in today’s data-driven world, opening up exciting industry opportunities. Now that you have a full answer to the question “What is machine learning?” here are compelling reasons why people should embark on the journey of learning ML, along with some actionable steps to get started.

• Unlocking Career Opportunities: ML expertise is highly sought after by employers in fields like data science, artificial intelligence, robotics, finance, healthcare, and more. Learning machine learning can pave the way for rewarding career paths and increased job prospects.
• Driving Innovation and Problem-Solving: It enables individuals to tackle complex problems, make data-driven decisions, and develop innovative solutions. Acquiring ML skills empowers individuals to create cutting-edge applications, drive technological advancements, and contribute to societal progress.
• Embracing the Future of Technology: Machine learning is at the forefront of technological advancements, shaping the future of automation, intelligent systems, and predictive analytics. Individuals can actively participate in and shape the evolving digital landscape by learning ML.

Now, let’s explore some steps to get started with machine learning.

• Gain a Solid Foundation in Mathematics and Statistics: Familiarize yourself with key mathematical concepts such as linear algebra, calculus, and probability theory. Understanding statistics is crucial for data analysis and model evaluation.
• Learn Programming: Start by learning a programming language commonly used in ML, such as Python or R. These languages offer extensive libraries and frameworks specifically designed for machine learning tasks.
• Take Online Courses and Tutorials: Online learning platforms offer many resources to learn ML. Explore upskilling platforms, which provide comprehensive machine learning bootcamps taught by industry experts and academics.
• Practice with Real-world Data Sets: Apply your knowledge by working on real-world data sets. Platforms like Kaggle offer data sets and competitions that allow you to solve practical problems and learn from the community.
• Join Communities: Engage with the ML community through forums, discussion groups, and social media platforms. Participating in discussions and collaborating with others can enhance your learning experience.
• Build Projects and Apply Your Knowledge: Put your skills to the test by working on machine learning projects . Start with simple projects, gradually progressing to more complex ones. Building projects helps solidify your understanding and showcases your abilities to potential employers.
• Stay Updated and Continuously Learn: ML is a rapidly evolving field. Stay updated with the latest research papers, attend conferences, and follow influential figures in the field to keep abreast of advancements.

Remember, learning ML is a journey that requires dedication, practice, and a curious mindset. By embracing the challenge and investing time and effort into learning, individuals can unlock the vast potential of machine learning and shape their own success in the digital era.

You might also like to read:

What Artificial Intelligence Engineer Salary Can You Expect?

Machine Learning Engineer Salary

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Unveiling the Machine Learning Process: Your Introduction to Solving Complex Problems with Data

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Picture this — you’re learning to ride a bike. Remember how you learned to balance, pedal, and steer? It took a bit of stumbling, perhaps a few grazed knees. But with every fall, you learned something new, adjusting your balance and your grip, getting better each time until you were cycling like a pro!

Well, machine learning isn’t too dissimilar. It is a process where we teach computers to learn from data, much like how you learned to ride that bike. But instead of scraped knees, we have algorithms sifting through vast oceans of data, learning, adjusting, and improving, bit by bit.

The Essence of the Machine Learning Process

The machine learning process is a roadmap, a sequence of stages meticulously designed to create, train, and deploy a machine learning model. This is not just a process; it’s a journey, a saga of transforming raw data into knowledge and finding solutions hidden in the labyrinth of information. Through this process, we can craft systems that make predictions and decisions based on data, shedding the constraints of explicitly programmed rules. It gifts machines the ability to learn from the data they are fed, aiding in identifying patterns and trends that are not immediately obvious. Moving beyond rigid rule-based systems, machine learning facilitates the automation of decision-making, heralding a revolution in how we interact with technology.

At the heart of the machine learning process is data —a vast, rich, and diverse repository of information waiting to be explored and understood. An awe-inspiring aspect of this process is the system’s ability to improve over time, learning diligently from experience. This journey of continuous learning enables the creation of intelligent systems capable of gaining insights and adapting based on new data encountered. Without the machine learning process, we would find ourselves ensnared in an endless cycle of manual coding for every conceivable action or decision. Fortunately, machine learning models can learn and update their knowledge autonomously , ushering in an era of efficiency and intelligence.

As we venture further, we begin to grasp the machine learning process’s potential to maximize the wealth that data harbors.  It can aid us in unearthing hidden relationships within data , shedding light on unseen patterns, and harvesting insights that fuel innovative, data-driven solutions to complex problems. The machine learning process stands as a sentinel of efficiency, automating analytical model building, thus saving time and streamlining processes, especially in large-scale systems.

One of the mesmerizing facets of the machine learning process is its ability to simplify complex problem-solving , breaking it down into manageable, distinct phases, each with a unique objective and a set of tasks to accomplish. This process encourages a focused approach, allowing us to zero in on individual phases addressing challenges efficiently and effectively.

Embrace the iterative nature of the machine learning process, a cycle of constant refinement and enhancement, enabling a dynamic evolution of the model that fosters continuous improvement .

The Blueprint of Machine Learning: A Step-by-Step Guide

1.   Understanding and Framing the Problem Before setting out on any adventure, it is essential to have a clear map and a defined destination. Similarly, the initial step in the machine learning process is to comprehend and frame the problem succinctly.

Defining the problem begins with laying out the business questions or challenges we aim to solve with our machine learning model. It’s the stage where curiosity meets clarity, setting the stage for the adventures ahead. Based on the problem at hand, we then decide on the type of machine learning model to apply. Be it supervised learning, guiding the model with labeled data; unsupervised learning, letting it find hidden patterns on its own; or reinforcement learning, where the model learns through trial and error, much like learning to ride a bicycle!

2.   Gathering and Preparing the Training Data With a clear objective in sight, we move to assemble our toolkit— the data. This step is pivotal in shaping the learning journey of our model. In order to accomplish this task, we must engage in:

• Data Collection: Amass data pertinent to the problem, forming the bedrock of our model’s learning landscape.
• Data Cleaning : Just as we wouldn’t add unnecessary ingredients while cooking, we clean the data, eliminating superfluous information.
• Data Preparation: When preparing data, we tailor it to suit the needs of our model, setting the stage for the training phase.
• Quality and Quantity: Remember, the efficacy of our model hinges significantly on the quality and the quantity of the training data, determining its predictive prowess.

3.   Training and Evaluating the Model As we journey further, we encounter the training grounds, where our model learns to find patterns and relationships within the data. During the training phase, the model immerses itself in the prepared data, learning to decipher patterns and understand relationships.

Post training, it’s time for a test! We evaluate the model using various measures such as accuracy, precision, or the F1 score  to understand its proficiency. If the model doesn’t meet the standards, fear not! We revisit previous steps, tweaking and tuning, in a relentless pursuit of excellence, teaching our model to be better.

4.   Deployment and Sharing the Model with Others With the testing phase behind us, we deploy our model in a production environment, ready to make predictions on new data and assist in solving real-world problems. We then open the doors to the world, sharing our model through APIs for integration with other software or through insightful visualizations and reports, helping stakeholders grasp the fruits of our labor.

Chris’s Playlist Challenge: A Machine Learning Solution

Imagine Chris, a high school student who loves music just as much as his friend Sasha does. However, they both have diverse tastes in music. Chris is more into rock, while Sasha has a penchant for indie tunes. Chris wants to surprise Sasha by creating a playlist of songs that Sasha hasn’t heard before but would most likely enjoy. It sounds like a challenging task, doesn’t it? Well, let’s see how Chris can use the machine learning process to his advantage:

Chris begins by clearly defining his goal: to find undiscovered songs that align with Sasha’s music preferences. He decides to focus on identifying indie songs from less-known artists, assuming that Sasha hasn’t heard them yet.

To gather the necessary data, Chris notes down the songs and artists that Sasha has mentioned loving in the past. He even asks Sasha subtly about the characteristics she enjoys in a song without giving away his plan. He compiles a list of these preferences, which forms the data that will train his machine learning model.

Chris, then, uses this data to train his machine learning model, teaching it to understand the patterns and relationships in Sasha’s music preferences. It’s a phase of learning and fine-tuning where Chris might discover that Sasha has a soft spot for soulful lyrics or acoustic elements. After training the model with this data, Chris tests it by having it predict Sasha’s potential liking for a small set of songs. He reviews the predictions to see if they align with what he knows about Sasha’s tastes.

Satisfied with the machine learning model’s understanding, Chris uses it to curate a list of new songs that align with Sasha’s taste profile. It feels like magic as the model identifies songs Chris wouldn’t have thought of on his own.

Once Sasha listens to the playlist, Chris asks for her feedback. This is a crucial step because Sasha’s feedback will help Chris refine the model further. Machine learning is a continuous cycle of learning and improving, and Chris plans to use this feedback for any future surprises he has for Sasha, ensuring that every new playlist is just a bit more perfect than the last one.

Through this fun and thoughtful project, Chris not only surprises Sasha with a personalized playlist but also experiences firsthand the incredible potential of the machine learning process in solving complex problems and creating something truly special from a series of data points.

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Machine learning is a method of data analysis that automates analytical model building. In simple terms, machine learning is “making a machine learn”. Machine learning is a new field that combines many traditional disciplines. It is a subset of AI.

What is ML pipeline?

• ML pipeline expresses the workflow by providing a systematic way on how to proceed with the machine learning model.
• ML pipelines automate the process of machine learning and following the pipeline makes the process of making ML models systematic and easy.

Here is the diagrammatic view of the ML pipeline:

MACHINE LEARNING PIPELINE

• The Machine Learning pipeline starts with data collection and integration. After data is collected analysis and visualization of data is done. Further, the most crucial step feature selection and engineering is performed then the model is trained. After that model, evaluation is done and our model becomes ready for prediction!
• To understand the pipeline well, consider building an ML model for a company’s customer care service. Consider a company XYZ, as an online book shop, that delivers books and kindle to its customer and this company wants an improved customer care service. It wants that if a customer calls the helpline for any kind of issue, say for replacement of books, complaint of kindle purchased or some other services. The company wants to ensure that the customer’s call gets directed to the right service person in minimum time and that the process should be smooth. To build a model for customer care services of the company, we will use the ML pipeline for the systematic development of the model.

1. Data Collection and integration: 

• The first step of the ML pipeline involves the collection of data and integration of data.
• Data collected acts as an input to the model (data preparation phase)
• Inputs are called features.
• Data collected in the case of our considered example involves a lot of data. The collected data should answer the following questions- What is past customer history? What were the past orders? Is the customer a prime member of our bookstore? Does the customer own a kindle? Has the customer made any previous complaints? What was the most number of complaints?
• The more the data is, more the better our model becomes.
• Once the data is collected we need to integrate and prepare the data.
• Integration of data means placing all related data together.
• Then data preparation phase starts in which we manually and critically explore the data.
• The data preparation phase tells the developer that is the data matching the expectations. Is there enough info to make an accurate prediction? Is the data consistent?

2. Exploratory Data Analysis and Visualisation:

• Once the data is prepared developer needs to visualize the data to have a better understanding of relationships within the dataset.
• When we get to see data, we can notice the unseen patterns that we may not have noticed in the first phase.
• It helps developers easily identify missing data and outliers.
• Data visualization can be done by plotting histograms, scatter plots, etc.
• After visualization is done data is analyzed so that developer can decide what ML technique he may use.
• In the considered example case unsupervised learning may be used to analyze customer purchasing habits.

3. Feature Selection and Engineering: 

• Feature selection means selecting what features the developer wants to use within the model.
• Features should be selected so that a minimum correlation exists between them and a maximum correlation exists between the selected features and output.
• Feature engineering is the process to manipulate the original data into new and potential data that has a lot many features within it.
• In simple words Feature engineering is converting raw data into useful data or getting the maximum out of the original data.
• Feature engineering is arguably the most crucial and time-consuming step of the ML pipeline.
• Feature selection and engineering answers questions – Are these features going to make any sense in our prediction?
• It deals with the accuracy and precision of data.

4.  Model Training: 

• After the first three steps are done completely we enter the model training phase.
• It is the first step officially when the developer gets to train the model on basis of data.
• To train the model, data is split into three parts- Training data, validation data, and test data.
• Around 70%-80% of data goes into the training data set which is used in training the model.
• Validation data is also known as development set or dev set and is used to avoid overfitting or underfitting situations i.e. enabling hyperparameter tuning.
• Hyperparameter tuning is a technique used to combat overfitting and underfitting.
• Validation data is used during model evaluation.
• Around 10%-15% of data is used as validation data.
• Rest 10%-15% of data goes into the test data set. Test data set is used for testing after the model preparation.
• It is crucial to randomize data sets while splitting the data to get an accurate model.
• Data can be randomized using Scikit learn in python.

5. Model Evaluation: 

• After the model training, validation, or development data is used to evaluate the model.
• To get the most accurate predictions to test data may be used for further model evaluation.
• A confusion matrix is created after model evaluation to calculate accuracy and precision numerically.
• After model evaluation, our model enters the final stage that is prediction.

6. Prediction:  

• In the prediction phase developer deploys the model.
• After model deployment, it becomes ready to make predictions.
• Predictions are made on training data and test data to have a better understanding of the build model.

The deployment of the model isn’t a one-time exercise. As more and more data gets generated, the model is trained on new data, evaluated again, and deployed again. Model training, model evaluation, and prediction phase circulate each other.                 

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While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Researchers from MIT and ETH Zurich used machine learning to speed things up.

They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu wrote the paper with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

Tough to solve

MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.  

“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems. 

Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

Shrinking the solution space

She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

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By: MIT xPRO on August 5th, 2020 3 Minute Read

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Here are the Most Common Problems Being Solved by Machine Learning

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Although machine learning offers important new capabilities for solving today’s complex problems, more organizations may be tempted to apply machine learning techniques as a one-size-fits all solution. 

To use machine learning effectively, engineers and scientists need a clear understanding of the most common issues that machine learning can solve. In a recent MIT xPRO Machine Learning whitepaper titled  " Applications For Machine Learning In Engineering and the Physical Sciences,” Professor Youssef Marzouk and fellow MIT colleagues outlined the potentials and limitations of machine learning in STEM. 

Here are some common challenges that can be solved by machine learning:

Accelerate processing and increase efficiency Machine learning can wrap around existing science and engineering models to create fast and accurate surrogates, identify key patterns in model outputs, and help further tune and refine the models. All this helps more quickly and accurately predict outcomes at new inputs and design conditions.

Quantify and manage risk. Machine learning can be used to model the probability of different outcomes in a process that cannot easily be predicted due to randomness or noise. This is especially valuable for situations where reliability and safety are paramount.

Compensate for missing data. Gaps in a data set can severely limit accurate learning, inference, and prediction. Models trained by machine learning improve with more relevant data. When used correctly, machine learning can also help synthesize missing data that round out incomplete datasets.

Make more accurate predictions or conclusions from your data . You can streamline your data-to-prediction pipeline by tuning how your machine learning model’s parameters will be updated and learning during training. Building better models of your data will also improve the accuracy of subsequent predictions.

Solve complex classification and prediction problems. Predicting how an organism’s genome will be expressed or what the climate will be like in fifty years are examples of highly complex problems. Many modern machine learning problems take thousands or even millions of data samples (or far more) across many dimensions to build expressive and powerful predictors, often pushing far beyond traditional statistical methods.

Create new designs. There is often a disconnect between what designers envision and how products are made. It’s costly and time-consuming to simulate every variation of a long list of design variables. Machine learning can identify key variables, automatically generate good options, and help designers identify which best fits their requirements.

Increase yields. Manufacturers aim to overcome inconsistency in equipment performance and predict maintenance by applying machine learning to flag defects and quality issues before products ship to customers, improve efficiency on the production line, and increase yields by optimizing the use of manufacturing resources.

Machine learning is undoubtedly hitting its stride, as engineers and physical scientists leverage the competitive advantage of big data across industries — from aerospace, to construction, to pharmaceuticals, transportation, and energy. But it has never been more important to understand the physics-based models, computational science, and engineering paradigms upon which machine learning solutions are built.

The list above details the most common problems that organizations can solve with machine learning. For more specific applications across engineering and the physical sciences, download MIT xPRO’s free Machine Learning whitepaper .

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Title: solving machine learning problems.

Abstract: Can a machine learn Machine Learning? This work trains a machine learning model to solve machine learning problems from a University undergraduate level course. We generate a new training set of questions and answers consisting of course exercises, homework, and quiz questions from MIT's 6.036 Introduction to Machine Learning course and train a machine learning model to answer these questions. Our system demonstrates an overall accuracy of 96% for open-response questions and 97% for multiple-choice questions, compared with MIT students' average of 93%, achieving grade A performance in the course, all in real-time. Questions cover all 12 topics taught in the course, excluding coding questions or questions with images. Topics include: (i) basic machine learning principles; (ii) perceptrons; (iii) feature extraction and selection; (iv) logistic regression; (v) regression; (vi) neural networks; (vii) advanced neural networks; (viii) convolutional neural networks; (ix) recurrent neural networks; (x) state machines and MDPs; (xi) reinforcement learning; and (xii) decision trees. Our system uses Transformer models within an encoder-decoder architecture with graph and tree representations. An important aspect of our approach is a data-augmentation scheme for generating new example problems. We also train a machine learning model to generate problem hints. Thus, our system automatically generates new questions across topics, answers both open-response questions and multiple-choice questions, classifies problems, and generates problem hints, pushing the envelope of AI for STEM education.

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How to choose your ai problem-solving tool in machine learning.

For a computer to perform a task, it must have a set of instructions to follow - which we provide. But machine learning - a subset of artificial intelligence (AI) - is quite different. It involves training computers to learn to do things. This approach can range from simple to very complex, based on the issue we want computers to address, and involves the use of various AI problem-solving tools.

Why is problem-solving important in artificial intelligence?

The ultimate aim of artificial intelligence is to create systems that can solve real-world problems. It does this by employing efficient and logical algorithms, utilizing polynomial and differential equations, and executing them using modeling paradigms. Such problem-solving techniques improve the performance of machine learning models so that they can ultimately be used in real-world applications.

AI systems themselves must overcome several barriers. Some of the major types of obstacles to problem-solving include unnecessary constraints and irrelevant information. A single problem may have unique or various solutions which are achieved by different heuristics.

This article will explore some of the things to consider when choosing an AI problem-solving tool as well as the various types of in-demand tools currently available.

How to choose the right artificial intelligence problem-solving tool

Real-world problems are often complex and involve having to deal with massive amounts of data. A single machine learning tool cannot fix all problems but a group of them can provide prospective solutions.

Before selecting a tool, consider a few things:

• Analyze the problem.
• Prioritize what you want from the tool.
• Be clear with your expectations.
• Compare different tools.
• Consider a tool that provides updated service with every change.
• Assess your model’s metadata such as experiment metrics, data versions, training parameters, etc.

In-demand artificial intelligence tools

While there are many AI problem-solving tools, the ones listed below are among the most sought-after.

TensorFlow is a free and open-source library developed by Google for machine learning and artificial intelligence applications. It takes input data in the form of tensors which are multi-dimensional arrays of higher dimensions. These multi-dimensional arrays are great at handling large amounts of data.

One of the reasons for the popularity of TensorFlow is that developers can easily build and deploy applications. TensorFlow works on the basis of data flow graphs, and can easily be executed in a distributed manner across a cluster of computers while using GPUs.

The following are the machine learning algorithms supported by TensorFlow:

• Linear regression: tf.estimator.LinearRegressor
• Classification: tf.estimator.LinearClassifier
• Boosted tree classification: tf.estimator.BoostedTreesClassifier
• Deep learning wipe and deep: tf.estimator.DNNLinearCombinedClassifier
• Boosted tree regression: tf.estimator.BoostedTreesRegressor
• Deep learning classification: tf.estimator.DNNClassifier

TensorFlow is best suited for applications such as classification, perception, understanding, discovering, prediction and creation.

Keras is a powerful open-source high-level neural network library. It uses Theano, TensorFlow, or CNTK at the back-end which acts as a high-level API wrapper for the low-level API. It supports both convolutional and recurrent neural networks as well as a combination of both.

Keras is easy to understand and supports multiple backends. A huge amount of data can be easily processed. The speed of training models is also higher as it can be run on multiple GPU instances at the same time. Keras can be one of the best tools for building neural network models in a user-friendly way.

Scikit-learn

Scikit -learn is a robust open-source tool for machine learning and statistical modeling. It was built on top of NumPy, SciPy, and matplotlib. It can be used to implement a wide range of algorithms including support vector machines, random forests, gradient boosting, k-means, etc.

Scikit-learn can be used for:

Supervised models, such as classification, regression, clustering

• Ensemble methods
• Feature extraction
• Feature selection
• Preprocessing
• Cross-validation
• Model selection
• Dimensionality reduction.

PyTorch is an open-source machine learning library that was developed by using Torch - a library for Python programming. PyTorch can be used to build complex neural networks easily. It has support for GPU and CPU, and supports cloud platforms.

ML and AI developers will find PyTorch easy to learn and build models with.

The features provided are:

• Autograd module
• Optim module

PyTorch is one of the emerging trends in the machine learning field and is being increasingly applied in industries. It can extensively be used for computer vision, deep learning, natural language processing, and reinforcement learning applications.

XGBoost stands for Extreme Gradient Boost. It is an open-source machine learning algorithm that is mainly used for implementing gradient boosting decision trees. Decision trees can be considered the best algorithm for structured/semi-structured data.

XGBoost greatly improves the speed and performance of ML models. It supports tree learning algorithm and linear model learning, making it suitable for parallel computation on a single machine. Hence, it is 10 times faster than all other algorithms. It also offers a good number of advanced features, one being scikit-learn regularization.

XGBoost can be used to solve problems in

• Classification
• User-defined prediction challenges

Catalyst is a machine learning framework built on top of PyTorch and is designed specifically for deep learning problems. It simplifies researchers’ tasks through features such as code reusability and reproducibility as well as by supporting faster experimentation. Catalyst enables developers to solve complex problems with few lines of code. It also offers a range of deep learning models like one-cycle training, range optimizer, etc.

Caffe2 is a lightweight, open-source machine learning tool and an updated version of Caffe. It provides n number of machine learning libraries through which complex models can easily be built and run. It supports mobile deployment and, hence, offers higher optimization for developers. It is used in computer vision , speech recognition, translation, chatbots, IoT , and medical applications.

OpenNN is an open-source machine learning tool for neural networks, and is the most successful method of ML to implement neural networks. OpenNN is used to solve many real-world applications in the fields of marketing, health, and more. It consists of many sophisticated algorithms that help to provide solutions for artificial intelligence problems.

OpenNN is best suited for solving issues involving:

• Forecasting
• Association

Apache Spark MLlib

Apache Spark MLlib is an open-source distributed machine learning framework built on top of Apache Spark core. Since it works on in-memory computation, it is nine times faster when compared to other disk-based implementations. It has a good number of ML libraries too which makes training of machine learning models easier. It also provides algorithms such as:

• Decision trees
• Collaborative filters
• Pipeline APIs of higher levels

Other machine learning tools

There are many other machine learning tools that help build and deploy models efficiently such as:

• Theano, which can be used for limited GPU resources with high speed
• ML.NET, for .NET developers
• LightGBM, for working with large datasets
• Weka tool, which provides machine learning algorithms for data mining
• Accord.NET, which helps in image and audio processing.

As discussed, always perform a complete analysis of your requirements as well as AI problem-solving tools before choosing one. Sometimes, a well-known tool may not necessarily be the right one for your project.

Considering the sheer number of ML tools available today, selecting the best is no easy task. Each has its advantages but may not be capable of addressing all your requirements. A combination of them can sometimes be the best way to get sound results.

1. What are the main problems that AI can solve?

AI can solve many real-world problems including enabling personalized shopping, fraud detection, virtual assistance, voice assistance, spam filtering, facial recognition, and recommendation systems. It can also be applied to common game problems such as water jug, travelling salesman, magic squares, Tower of Hanoi, sudoku, N Queen, chess, crypt-arithmetic, logical puzzles, etc.

2. What are problem-solving techniques in AI?

Problems in artificial intelligence can be solved by using techniques such as searching algorithms, genetic algorithms, evolutionary computations, knowledge representations, etc.

3. What is the role of AI in real-world problem solving?

One of the biggest benefits of AI is its ability to solve many real-world problems. AI problem-solving techniques can be applied in the fields of marketing, banking, gaming, healthcare, finance, virtual assistance, agriculture, space exploration, and autonomous vehicles, to name a few.

4. What problems can AI not solve?

AI is not suitable for creating, conceptualizing, or planning strategically. It can’t deal with unstructured and unknown spaces, especially ones it hasn’t experienced before. It can’t interact or feel compassion and empathy. Without training data, it can’t do anything meaningful.

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• Machine Learning for SQL Use Cases

Machine Learning Process

The lifecycle of a machine learning project is divided into six phases. The process begins by defining a business problem and restating the business problem in terms of a machine learning objective. The end goal of a machine learning process is to produce accurate results for solving your business problem.

The machine learning process workflow illustration is based on the CRISP-DM methodology. Each stage in the workflow is illustrated with points that summarize the key tasks. The CRISP-DM methodology is the most commonly used methodology for machine learning.

• Define business goals
• Understand data
• Prepare data
• Develop models

Figure 1-3 Machine Learning Process Workflow

Related Topics

• https://www.datasciencecentral.com/profiles/blogs/crisp-dm-a-standard-methodology-to-ensure-a-good-outcome
• https://www.sv-europe.com/crisp-dm-methodology/

Define Business Goals

The first phase of machine learning process is to define business objectives. This initial phase of a project focuses on understanding the project objectives and requirements.

Once you have specified the problem from a business perspective, you can formulate it as a machine learning problem and develop a preliminary implementation plan. Identify success criteria to determine if the machine learning results meet the business goals defined. For example, your business problem might be: "How can I sell more of my product to customers?" You might translate this into a machine learning problem such as: "Which customers are most likely to purchase the product?" A model that predicts who is most likely to purchase the product is typically built on data that describes the customers who have purchased the product in the past.

To summarize, in this phase, you will:

• Specify objectives
• Determine machine learning goals
• Define success criteria
• Produce project plan

Understand Data

The data understanding phase involves data collection and exploration which includes loading the data and analyzing the data for your business problem.

• Is the data complete?
• Are there missing values in the data?
• What types of errors exist in the data and how can they be corrected?
• Access and collect data
• Explore data
• Assess data quality

Prepare Data

The preparation phase involves finalizing the data and covers all the tasks involved in making the data in a format that you can use to build the model.

Data preparation tasks are likely to be performed multiple times, iteratively, and not in any prescribed order. Tasks can include column (attributes) selection as well as selection of rows in a table. You may create views to join data or materialize data as required, especially if data is collected from various sources. To cleanse the data, look for invalid values, foreign key values that don't exist in other tables, and missing and outlier values. To refine the data, you can apply transformations such as aggregations, normalization, generalization, and attribute constructions needed to address the machine learning problem. For example, you can transform a DATE_OF_BIRTH column to AGE ; you can insert the median income in cases where the INCOME column is null; you can filter out rows representing outliers in the data or filter columns that have too many missing or identical values.

Additionally you can add new computed attributes in an effort to tease information closer to the surface of the data. This process is referred as Feature Engineering . For example, rather than using the purchase amount, you can create a new attribute: "Number of Times Purchase Amount Exceeds $500 in a 12 month time period." Customers who frequently make large purchases can also be related to customers who respond or don't respond to an offer.

Oracle Machine Learning supports Automatic Data Preparation (ADP), which greatly simplifies the process of data preparation.

• Clean, join, and select data
• Transform data
• Engineer new features
• Oracle Machine Learning for SQL User’s Guide

Develop Models

In this phase, you select and apply various modeling techniques and tune the algorithm parameters, called hyperparameters , to desired values.

If the algorithm requires specific data transformations, then you need to step back to the previous phase to apply them to the data. For example, some algorithms allow only numeric columns such that string categorical data must be "exploded" using one-hot encoding prior to modeling. In preliminary model building, it often makes sense to start with a sample of the data since the full data set might contain millions or billions of rows. Getting a feel for how a given algorithm performs on a subset of data can help identify data quality issues and algorithm setting issues sooner in the process reducing time-to-initial-results and compute costs. For supervised learning problem, data is typically split into train (build) and test data sets using an 80-20% or 60-40% distribution. After splitting the data, build the model with the desired model settings. Use default settings or customize by changing the model setting values. Settings can be specified through OML's PL/SQL, R and Python APIs. Evaluate model quality through metrics appropriate for the technique. For example, use a confusion matrix, precision, and recall for classification models; RMSE for regression models; cluster similarity metrics for clustering models and so on.

Automated Machine Learning (AutoML) features may also be employed to streamline the iterative modeling process, including algorithm selection, attribute (feature) selection, and model tuning and selection.

• Explore different algorithms
• Build, evaluate, and tune models

Splitting the Data

Separate data sets are required for building (training) and testing some predictive models. Typically, one large table or view is split into two data sets: one for building the model, and the other for testing the model.

The build data (training data) and test data must have the same column structure. The process of applying the model to test data helps to determine whether the model, built on one chosen sample, is generalizable to other data.

You need two case tables to build and validate supervised (like classification and regression) models. One set of rows is used for training the model, another set of rows is used for testing the model. It is often convenient to derive the build data and test data from the same data set. For example, you could randomly select 60% of the rows for training the model; the remaining 40% could be used for testing the model. Models that implement unsupervised machine learning techniques , such as attribute importance, clustering, association, or feature extraction, do not use separate test data.

At this stage of the project, it is time to evaluate how well the model satisfies the originally-stated business goal.

During this stage, you will determine how well the model meets your business objectives and success criteria. If the model is supposed to predict customers who are likely to purchase a product, then does it sufficiently differentiate between the two classes? Is there sufficient lift? Are the trade-offs shown in the confusion matrix acceptable? Can the model be improved by adding text data? Should transactional data such as purchases (market-basket data) be included? Should costs associated with false positives or false negatives be incorporated into the model?

It is useful to perform a thorough review of the process and determine if important tasks and steps are not overlooked. This step acts as a quality check based on which you can determine the next steps such as deploying the project or initiate further iterations, or test the project in a pre-production environment if the constraints permit.

• Review business objectives
• Assess results against success criteria
• Determine next steps

Deployment is the use of machine learning within a target environment. In the deployment phase, one can derive data driven insights and actionable information.

Deployment can involve scoring (applying a model to new data), extracting model details (for example the rules of a decision tree), or integrating machine learning models within applications, data warehouse infrastructure, or query and reporting tools.

Because Oracle Machine Learning builds and applies machine learning models inside Oracle Database, the results are immediately available. Reporting tools and dashboards can easily display the results of machine learning. Additionally, machine learning supports scoring single cases or records at a time with dynamic, batch, or real-time scoring. Data can be scored and the results returned within a single database transaction. For example, a sales representative can run a model that predicts the likelihood of fraud within the context of an online sales transaction.

• Plan enterprise deployment
• Integrate models with application for business needs
• Monitor, refresh, retire, and archive models
• Report on model effectiveness

Supervised Learning: Testing

The process of applying the model to test data helps to determine whether the model, built on one chosen sample, is generalizable to other data. In other words, test data is used for scoring.

In particular, it helps to avoid the phenomenon of overfitting, which can occur when the logic of the model fits the build data too well and therefore has little predictive power.

Supervised Learning: Scoring

Learn about scoring in supervised learning.

Apply data, also called scoring data, is the actual population to which a model is applied. For example, you might build a model that identifies the characteristics of customers who frequently buy a certain product. To obtain a list of customers who shop at a certain store and are likely to buy a related product, you might apply the model to the customer data for that store. In this case, the store customer data is the scoring data.

Most supervised learning can be applied to a population of interest. The principal supervised machine learning techniques, classification and regression , can both be used for scoring.

Oracle Machine Learning does not support the scoring operation for attribute importance , another supervised technique. Models of this type are built on a population of interest to obtain information about that population; they cannot be applied to separate data. An attribute importance model returns and ranks the attributes that are most important in predicting a target value.

Oracle Machine Learning supports the supervised machine learning techniques described in the following table:

Table 1-6 Oracle Machine Learning Supervised Techniques

Unsupervised Learning: Scoring

Introduces unsupervised learning, supported scoring operations, and unsupervised machine learning techniques.

Although unsupervised machine learning does not specify a target, most unsupervised learning can be applied to a population of interest. For exa mple, clustering models use descriptive machine learning techniques, but they can be applied to classify cases according to their cluster assignments. Anomaly Detection , although unsupervised, is typically used to predict whether a data point is typical among a set of cases.

Oracle Machine Learning supports the scoring operation for Clustering and Feature Extraction , both unsupervised machine learning techniques . Oracle Machine Learning does not support the scoring operation for Association Rules , another unsupervised function. Association models are built on a population of interest to obtain information about that population; they cannot be applied to separate data. An association model returns rules that explain how items or events are associated with each other. The association rules are returned with statistics that can be used to rank them according to their probability.

OML supports the unsupervised techniques described in the following table:

Table 1-7 Oracle Machine Learning Unsupervised Techniques

• Machine Learning Techniques
• In-Database Scoring

9 Real-World Problems that can be Solved by Machine Learning

Machine Learning has gained a lot of prominence in the recent years because of its ability to be applied across scores of industries to solve complex problems effectively and quickly. Contrary to what one might expect, Machine Learning use cases are not that difficult to come across. The most common examples of problems solved by machine learning are image tagging by Facebook and spam detection by email providers.

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AI for business can resolve incredible challenges across industry domains by working with suitable datasets. In this post, we will learn about some typical problems solved by machine learning and how they enable businesses to leverage their data accurately.

What is Machine Learning?

A sub-area of artificial intelligence, machine learning, is an IT system's ability to recognize patterns in large databases to find solutions to problems without human intervention. It is an umbrella term for various techniques and tools to help computers learn and adapt independently.

Unlike traditional programming, a manually created program that uses input data and runs on a computer to produce the output, in Machine Learning or augmented analytics, the input data and output are given to an algorithm to create a program. It leads to powerful insights that can be used to predict future outcomes.

Machine learning algorithms do all that and more, using statistics to find patterns in vast amounts of data that encompass everything from images, numbers, words, etc. If the data can be stored digitally, it can be fed into a machine-learning algorithm to solve specific problems.

Types Of Machine Learning

Today, Machine Learning algorithms are primarily trained using three essential methods. These are categorized as three types of machine learning, as discussed below –

    1. Supervised Learning

One of the most elementary types of machine learning, supervised learning, is one where data is labeled to inform the machine about the exact patterns it should look for. Although the data needs to be labeled accurately for this method to work, supervised learning is compelling and provides excellent results when used in the right circumstances.

For instance, when we press play on a Netflix show, we’re informing the Machine Learning algorithm to find similar shows based on our preference.

How it works –

• The Machine Learning algorithm here is provided with a small training dataset to work with, which is a smaller part of the bigger dataset.
• It serves to give the algorithm an idea of the problem, solution, and various data points to be dealt with.
• The training dataset here is also very similar to the final dataset in its characteristics and offers the algorithm with the labeled parameters required for the problem.
• The Machine Learning algorithm then finds relationships between the given parameters, establishing a cause and effect relationship between the variables in the dataset.

    2. Unsupervised Learning

Unsupervised learning, as the name suggests, has no data labels. The machine looks for patterns randomly. It means that there is no human labor required to make the dataset machine-readable. It allows much larger datasets to be worked on by the program. Compared to supervised learning, unsupervised Machine Learning services aren’t much popular because of lesser applications in day-to-day life. 

How does it work?

• Since unsupervised learning does not have any labels to work off, it creates hidden structures.
• Relationships between data points are then perceived by the algorithm randomly or abstractly, with absolutely no input required from human beings.
• Instead of a specific, defined, and set problem statement, unsupervised learning algorithms can adapt to the data by changing hidden structures dynamically.

    3. Reinforcement Learning

Reinforcement learning primarily describes a class of machine learning problems where an agent operates in an environment with no fixed training dataset. The agent must know how to work using feedback.

• Reinforcement learning features a machine learning algorithm that improves upon itself.
• It typically learns by trial and error to achieve a clear objective.
• In this Machine Learning algorithm, favorable outputs are reinforced or encouraged, whereas non-favorable outputs are discouraged.

Top 4 Issues with Implementing Machine Learning

While Machine learning is extensively used across industries to make data-driven decisions, its implementation observes many problems that must be addressed. Here’s a list of organizations' most common  machine learning challenges when inculcating ML in their operations.

1. Inadequate Training Data

Data plays a critical role in the training and processing of machine learning algorithms. Many data scientists attest that insufficient, inconsistent, and unclean data can considerably hamper the efficacy of ML algorithms.

2. Underfitting of Training Data

This anomaly occurs when data fails to link the input and output variables explicitly. In simpler terms, it means trying to fit in an undersized t-shirt. It indicates that data isn’t too coherent to forge a precise relationship.

3. Overfitting of Training Data

Overfitting denotes an ML model trained with enormous amounts of data that negatively affects performance. It's similar to trying an oversized jeans.

4. Delayed Implementation

ML models offer efficient results but consume a lot of time due to data overload, slow programs, and excessive requirements. Additionally, they demand timely monitoring and maintenance to deliver the best output.

9 Real-World Problems Solved by Machine Learning

Applications of Machine learning are many, including external (client-centric) applications such as product recommendation , customer service, and demand forecasts, and internally to help businesses improve products or speed up manual and time-consuming processes.

Machine learning algorithms are typically used in areas where the solution requires continuous improvement post-deployment. Adaptable machine learning solutions are incredibly dynamic and are adopted by companies across verticals.

Here we are discussing nine Machine Learning use cases –

    1. Identifying Spam

Spam identification is one of the most basic applications of machine learning. Most of our email inboxes also have an unsolicited, bulk, or spam inbox, where our email provider automatically filters unwanted spam emails. 

But how do they know that the email is spam?

They use a trained Machine Learning model to identify all the spam emails based on common characteristics such as the email, subject, and sender content. 

If you look at your email inbox carefully, you will realize that it is not very hard to pick out spam emails because they look very different from real emails. Machine learning techniques used nowadays can automatically filter these spam emails in a very successful way. 

Spam detection is one of the best and most common problems solved by Machine Learning. Neural networks employ content-based filtering to classify unwanted emails as spam. These neural networks are quite similar to the brain, with the ability to identify spam emails and messages.

    2. Making Product Recommendations

Recommender systems are one of the most characteristic and ubiquitous machine learning use cases in day-to-day life. These systems are used everywhere by search engines, e-commerce websites (Amazon), entertainment platforms (Google Play, Netflix), and multiple web & mobile apps.

Prominent online retailers like Amazon and eBay often show a list of recommended products individually for each of their consumers. These recommendations are typically based on behavioral data and parameters such as previous purchases, item views, page views, clicks, form fill-ins, purchases, item details (price, category), and contextual data (location, language, device), and browsing history.  

These recommender systems allow businesses to drive more traffic, increase customer engagement, reduce churn rate, deliver relevant content and boost profits. All such recommended products are based on a machine learning model’s analysis of customer’s behavioral data. It is an excellent way for online retailers to offer extra value and enjoy various upselling opportunities using machine learning.

    3. Customer Segmentation

Customer segmentation, churn prediction and customer lifetime value (LTV) prediction are the main challenges faced by any marketer. Businesses have a huge amount of marketing relevant data from various sources such as email campaigns, website visitors and lead data.

Using data mining and machine learning , an accurate prediction for individual marketing offers and incentives can be achieved. Using ML, savvy marketers can eliminate guesswork involved in data-driven marketing.

For example, given the pattern of behavior by a user during a trial period and the past behaviors of all users, identifying chances of conversion to paid version can be predicted. A model of this decision problem would allow a program to trigger customer interventions to persuade the customer to convert early or better engage in the trial.

    4. Image & Video Recognition

Advances in deep learning (a subset of machine learning) have stimulated rapid progress in image & video recognition techniques over the past few years. They are used for multiple areas, including object detection, face recognition, text detection, visual search, logo and landmark detection, and image composition.

Since machines are good at processing images, Machine Learning algorithms can train Deep Learning frameworks to recognize and classify images in the dataset with much more accuracy than humans. 

Similar to image recognition , companies such as Shutterstock , eBay , Salesforce , Amazon , and Facebook use Machine Learning for video recognition where videos are broken down frame by frame and classified as individual digital images.

    5. Fraudulent Transactions

Fraudulent banking transactions are quite a common occurrence today. However, it is not feasible (in terms of cost involved and efficiency) to investigate every transaction for fraud, translating to a poor customer service experience.

Machine Learning in finance can automatically build super-accurate predictive maintenance models to identify and prioritize all kinds of possible fraudulent activities. Businesses can then create a data-based queue and investigate the high priority incidents.

It allows you to deploy resources in an area where you will see the greatest return on your investigative investment. Further, it also helps you optimize customer satisfaction by protecting their accounts and not challenging valid transactions. Such fraud detection using machine learning can help banks and financial organizations save money on disputes/chargebacks as one can train Machine Learning models to flag transactions that appear fraudulent based on specific characteristics.

    6. Demand Forecasting

The concept of demand forecasting is used in multiple industries, from retail and e-commerce to manufacturing and transportation. It feeds historical data to Machine Learning algorithms and models to predict the number of products, services, power, and more.

It allows businesses to efficiently collect and process data from the entire supply chain, reducing overheads and increasing efficiency.

ML-powered demand forecasting is very accurate, rapid, and transparent. Businesses can generate meaningful insights from a constant stream of supply/demand data and adapt to changes accordingly. 

    7. Virtual Personal Assistant

From Alexa and Google Assistant to Cortana and Siri, we have multiple virtual personal assistants to find accurate information using our voice instruction, such as calling someone, opening an email, scheduling an appointment, and more.

These virtual assistants use Machine Learning algorithms for recording our voice instructions, sending them over the server to a cloud, followed by decoding them using Machine Learning algorithms and acting accordingly.

    8. Sentiment Analysis

Sentiment analysis is one of the beneficial and real-time machine learning applications that help determine the emotion or opinion of the speaker or the writer. 

For instance, if you’ve written a review, email, or any other form of a document, a sentiment analyzer will be able to assess the actual thought and tone of the text. This sentiment analysis application can be used to analyze decision-making applications, review-based websites, and more.

    9. Customer Service Automation

Managing an increasing number of online customer interactions has become a pain point for most businesses. It is because they simply don’t have the customer support staff available to deal with the sheer number of inquiries they receive daily.

Machine learning algorithms have made it possible and super easy for chatbots and other similar automated systems to fill this gap. This application of machine learning enables companies to automate routine and low priority tasks, freeing up their employees to manage more high-level customer service tasks. 

Further, Machine Learning technology can access the data, interpret behaviors and recognize the patterns easily. This could also be used for customer support systems that can work identical to a real human being and solve all of the customers’ unique queries. The Machine Learning models behind these voice assistants are trained on human languages and variations in the human voice because it has to efficiently translate the voice to words and then make an on-topic and intelligent response.

If implemented the right way, problems solved by machine learning can streamline the entire process of customer issue resolution and offer much-needed assistance along with enhanced customer satisfaction.

Wrapping Up

As advancements in machine learning evolve, the range of use cases and applications of machine learning too will expand. To effectively navigate the business issues in this new decade, it’s worth keeping an eye on how machine learning applications can be deployed across business domains to reduce costs, improve efficiency and deliver better user experiences.

However, to implement machine learning accurately in your organization, it is imperative to have a trustworthy partner with deep-domain expertise. At Maruti Techlabs, we offer advanced machine learning services that involve understanding the complexity of varied business issues, identifying the existing gaps, and offering efficient and effective tech solutions to manage these challenges.

If you wish to learn more about how machine learning solutions can increase productivity and automate business processes for your business, get in touch with us .

Pinakin is the VP of Data Science and Technology at Maruti Techlabs. With about two decades of experience leading diverse teams and projects, his technological competence is unmatched.

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