30 Best Resources to Study Machine Learning

Machine learning is one of the most promising fields today. So, we decided to help you learn it.

If you have no experience in ML development, it’s okay: the post includes both introductory and advanced level materials. Pour yourself a cup of tea; this post is going to be looong. Let’s go.

Best machine learning courses

best ml courses

The further you go, the more advanced the ML materials are. You will surely find something that suits your goals.

Basics of machine learning for beginners

1. Elements of AI

It is a nice machine learning crash course for everyone who wants to understand how artificial intelligence or ML works. It contains crystal clear explanations and plenty of examples and tasks to test yourself. Whether you’re a business owner, freelance marketer, or any non-tech specialist, Elements of AI will be interesting for you.

This course, made by the University of Helsinki with the support of The Finnish Presidency of the Council of the EU, covers the basics of machine learning for beginners. It is aimed at increasing artificial intelligence awareness in the world.

2. CS50’s Introduction to Artificial Intelligence with Python


CS50 is a public Harvard-based course taught by David Malan, and it is the largest course in Harvard and on EdX, viewed by more than 1 million (!) people. That has to mean something when so many people want to listen to a guy talking about computer science. Malan knows how to tell complicated things in a way that they seem fascinating, entertaining, and easy to learn. If you’re not yet quite confident in your technical skills and want to learn about machine learning the fun way, CS50 is for you.

3. Python programming tutorials by Socratica

Youtube is the place where many talented people share their content, and sometimes you can stumble upon true masterpieces. Socratica is one of the best machine learning Youtube channels. Their Python programming tutorials are almost as fascinating as Netflix.

Python is the most popular programming language used for machine learning and data science projects. This language has many libraries and can be used for both backend and frontend programming. With libraries like Tensorflow and scikit-learn, you can easily start writing an AI system. So it is a good idea to grasp the basics of Python if you are interested in ML.

4. Google’s machine learning crash course

This machine learning with TensorFlow APIs course is Google’s practical introduction to machine learning. You will be able to use this self-study ML guide even if you have zero knowledge in ML. However, to be able to keep up, you need to have general programming skills and a mathematical background.

The course includes a series of video lectures, real-world case studies, and hands-on exercises that will teach you how to program machine learning algorithms. For more AI-related learning materials, visit Google’s educational platform.

5. ML and Big Data analytics course

To learn how to apply machine learning techniques in practice for data analytics, go to Big Data: Statistical Inference and Machine Learning. This course introduces you to statistical and machine learning tools (such as neural networks, decision trees, principal component analysis, and clustering) that can be used to work with large datasets and extract information. Then, you can exercise your coding skills to solve real-life tasks.

To follow the course more effectively, you will need university undergraduate-level knowledge of math and statistics.

6. Machine learning course from Stanford

mashine learning course stenford

This course provides a broad overview of machine learning, data mining, and statistical pattern recognition. It will teach you how to use common ML algorithms and apply the best practices to solving tasks. What is great about this course is that it contains many examples and real case studies that will show you the potential of AI and ML and their many applications. You will be able to use what you learned to solve many tasks, from text recognition to medical informatics and building smart robots.

While it is possible to study machine learning without programming knowledge, the mathematical background of a university graduate level is strongly recommended.

7. Machine learning with Python

IBM teaches you Python so that you could use this language to write outstanding machine learning programs. During the course, you will learn about supervised and unsupervised machine learning, discover hidden trends and get valuable insights. You will explore popular ML algorithms like Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. The course teaches you based on real-life examples and encourages you to notice how machine learning technologies change the reality we live in every day.

Best advanced courses in machine learning

These courses will allow you to dive deeper into machine learning mysteries.

8. Advanced Machine Learning

If you have completed introductory-level courses, you can discover more about data analytics and machine learning algorithms in this Future Learn course.

It will not make you a data scientist but will provide you with a better understanding of applying ML algorithms for data analysis. Having completed this course, you will be able to formulate a typical data analysis problem and perform the necessary steps to offer a solution. You will be able to evaluate the effectiveness of your statistical model and switch between different approaches for the sake of solving the task more efficiently.

9. Introduction to Computational Thinking and Data Science from MIT

Some see programming as merely a practical discipline, but in reality, it demands the ability to reason about problems in a particular way.

Massachusetts Institute of Technology will introduce you to the basic principles of computational thinking applied to data science. This course focuses on plotting with the pylab package, stochastic programming and statistical thinking, and Monte Carlo simulations. To follow the course effectively, you need prior knowledge of Python.

10. HarwardX’s Data Science

Harward data science lectures

Many dream of attending the best machine learning lectures at Harvard, but not many can actually afford it. With this online specialization, you will be able to earn a professional certificate from Harvard almost for free.

It will help you acquire fundamental R programming skills, grasp such statistical concepts such as probability, inference, and modeling, and how to apply them in practice. You will become familiar with essential tools for practicing data scientists and implement machine learning algorithms. Overall, this course allows you to get in-depth knowledge of fundamental data science concepts through motivating real-world case studies and lots of practice. The course takes about 1,5 years to complete, so get ready for a long journey. Otherwise, you might be interested in pursuing a professional certificate from IBM, which is a bit shorter (10 months).

11. Professional Certificate Program In AI And Machine Learning

This 11-month online program, sponsored by Purdue University and IBM, is designed for professionals with programming experience. It covers machine learning, natural language processing, neural networks, reinforcement learning, and a range of tools, including Python, Keras, TensorFlow, OpenAI gym, Alexa, Amazon SageMaker, etc. You will take part in regular live sessions from internationally recognized experts, labs, companies, and institutions, including exclusive hackathons from IBM. You will also work on projects with industry datasets from Uber, Twitter, Mercedes Benz, and many others.

Candidates are selected based on their applications. To be accepted for the course, you have to have a basic knowledge of programming concepts and mathematics and a bachelor’s degree with a grade point average of 50 percent or higher. If you have at least two years of work experience, that would be a definite advantage!

Focusing on a particular field

In this section, you will find some more materials that focus on more specific cases of machine learning application.

12. Data Science

If you got interested in the methods, processes, and algorithms that lay behind extracting knowledge and insights from data, learn data science. DataCamp is one of the best places to do that: the lessons are small and concise, so you will be able to make progress anywhere you go from your mobile device. The platform also offers immediate hands-on-the-keyboard exercises and a built-in practice mode that provides feedback on every exercise. Suitable even for absolute beginners.

13. Deep learning

You know what machine learning is. Possibly, you can even teach others. It’s time to concentrate on a more narrow field like deep learning. Deep learning is part of ML methods family based on feature/representation learning. For those who are in love with neural networks, this course provides plenty of possibilities to express yourself.

14. Computer vision

Deep learning in computer vision

Learn to apply deep learning algorithms to indexing and face recognition, photo stylization, or computer vision in self-driving cars. This course starts from the basics, gradually introducing you to image classification and annotation, object recognition and image search, and motion estimation techniques.

15. Natural language processing

Voice assistants, robots, and even some security systems all work using natural language processing systems. This NLP-oriented course focuses on computer-human interactions, in particular: how to teach computers to process, analyze, and perform valuable actions (like respond or translate). Sounds cool, huh?

16. Recommendation systems

You can learn to build recommender systems. These systems predict what the user might like based on other users’ experiences and can offer personalized recommendations. Learning how to design, build, and evaluate recommender systems for commerce and content platforms is fun. Moreover, it’s a nice skill to have in your programming CV.

17. Investment management with Data Science

With the help of top-notch technologies, investment becomes less risky. This course provided by EDHEC Business School will show you how to write software in Python useful for risk management, portfolio construction and analysis, and managing your own investments. You will also learn to implement data science techniques in investment decision making.

18. Machine learning for healthcare

Machine learning for healthcare

Want to save lives but you’re a programmer? Learn about data science applications in stratified healthcare and precision medicine. This course prepares specialists that are able to process large amounts of data such as genomic data, electronic patient records, and data collected by wearable devices for better medical diagnosis.

Your machine learning bookshelf

best ml books

Boost your knowledge with a couple of super smart books about the most relevant tools and techniques in machine learning.

Beginner’s corner

This is just what you need to get started with machine learning.

19. Machine Learning For Absolute Beginners: A Plain English Introduction by Oliver Theobald

“Machine Learning For Absolute Beginners” is an introductory-level book, which, however, teaches you a great deal about what you should know about ML: from how to download free datasets to the tools and machine learning libraries you will need. The book contains data scrubbing techniques, regression analysis, clustering, basics of neural networks, and many more tools that you need to successfully get started with machine learning. Available on Amazon.

20. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started by Drew Conway

If you already have some programming experience but would like to know more about ML and data science, this book is for you. It will help you to take off with machine learning. You will study lots of examples and get the necessary amount of theory (but not too much!). “Machine Learning for Hackers” focuses on specific problems in each chapter, such as classification, prediction, optimization, and recommendation. It will also teach you to analyze different sample datasets and write simple machine learning algorithms in R.

21. Machine Learning: The New AI by Ethem Alpaydin

The New AI by Ethem Alpaydin

“Machine Learning: The New AI” concentrates on basic cases of ML application. This book instructs you on machine learning algorithms for pattern recognition, artificial neural networks, reinforcement learning, data science, and the ethical and legal implications of ML for data privacy and security. By finishing this book, you will be able to get a full understanding of how digital technology advanced from number-crunching mainframes to mobile devices, putting today’s machine learning boom in context. You can buy this book online.

22. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

Data preparation is a fundamental step that determines the success of the entire work. This book will help you figure out how to organize your data storage to ensure scalability, consistency, reliability, efficiency, and maintainability of data. And what about various technologies, such as message brokers, stream or batch processors, relational databases, and NoSQL data stores? Which options are best for your application? How can you get the most out of them?

Author Martin Kleppmann compares the pros and cons of the various systems for processing and storing data and helps you navigate this complex environment. Although software is constantly evolving, the underlying concepts never change. With the help of this book, you will be able to put these concepts into practice and make the best use of data in modern applications.

Expert’s corner

Plenty of free resources for machine learning await you in this list.

23. Pattern Recognition and Machine Learning by Christopher M. Bishop

This book is great for understanding and using statistical techniques in machine learning and pattern recognition. It presents detailed practice exercises to guarantee a comprehensive introduction to the topic. Other areas that are covered in the book are approximate inference algorithms, Bayesian methods, introduction to basic probability theory, and new models based on kernels.

Good understanding of linear algebra and some experience with probability are prerequisites for going through this machine learning book. You can buy this book here.

24. Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis

Read this ML tutorial if you want to have a unified perspective on both probabilistic and deterministic approaches in machine learning. The book presents the major machine learning methods and their practical applications in statistics, statistical and adaptive signal processing, and computer science. All the various ML methods and techniques are explained in great detail and supported by examples and problem sets that provide the researcher with a profound understanding of machine learning concepts. This machine learning PDF is available for free.

25. Hands-On Machine Learning with Scikit-Learn & TensorFlow by Aurélien Géron

Aurélien Géron helps you to gain an intuitive understanding of the concepts and tools for building intelligent systems with the help of two Python frameworks – scikit-learn and TensorFlow. You will familiarize yourself with various techniques, starting with simple linear regression and progressing to deep neural networks. Each chapter includes exercises that will encourage you to apply what you have learned.

26. Natural Language Processing by Jacob Eisenstein

This textbook provides a technical perspective on natural language processing. It emphasizes contemporary data-driven approaches and contains methods for building computer software that understands, generates, and manipulates human language. The book can be used by undergraduate and graduate-level students and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. Available on GitHub.

27. Gaussian Processes for Machine Learning by C.E.Rasmussen & C.K.I.Williams

This book offers a comprehensive introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have enjoyed increased attention over the past decade, so this book provides a systematic and unified overview of their theoretical and practical aspects in machine learning.

The book includes plenty of examples and exercises, and code and datasets are available on the web.

More information about cool books for studying ML is available on Hackr.io. If you have any questions or valuable resources in mind, feel free to tweet them to us. Let’s share some knowledge!

Bonus: ML training platforms

machine learning training platforms

28. Amazon machine learning courses

Amazon’s educational platform was previously available only for employees, but now anyone can take advantage of it free of charge. You get access to more than 30 courses in total. The content addresses beginners, intermediate-level specialists, and advanced developers.

The course starts with fundamental concepts and builds on those through real-world examples. You will be able to get a sneak peek into the technologies that stand behind AmazonGo and Amazon’s e-commerce solutions by optimizing delivery routes or predicting entertainment award nominations based on data from IMDb’s database.

Amazon educational platform

The courses are free, but if you are going to build something, you need to pay for cloud services that Amazon uses for lab testing. It is also possible to pass an exam for an “AWS Certified Machine Learning – Specialty” certification for the price of $300.

29. Kaggle Progression System

Kaggle is an international data science community that offers some of the best free machine learning courses online. It provides plenty of resources and tools on your way to becoming a data scientist. Kaggle offers materials separated into 5 categories: novice, contributor, expert, master, and grandmaster. By participating in competitions and discussions, you boost your expertise and the necessary ML skills. Feel free to explore this platform, and you won’t be disappointed. You might want to start with this Introduction to Machine Learning. If you feel more confident about yourself, try to solve the Titanic problem or predict sales prices for houses. Good luck on your way to becoming the Grandmaster of Data Science!

30. Neptune

Neptune is a simple experiment management tool that allows you to train ML models, verify hypotheses, and track the results of your machine learning experiments. It has excellent scalability, extensive visualization capabilities and is compatible with many frameworks. Neptune is a great platform for personal research, providing you with 200 free monitoring hours each month and a storage limit of 100 GB.

Final thoughts

There is no right or wrong way to get started with ML. You can begin by learning Python or first grasping calculus and statistics. Whatever path you choose, the wonderful thing is that by studying machine learning, you get access to one of the most advanced technologies in the world. Computers are smart, but they still can’t learn on their own. They need your help!

Article updated on 25 September 2022 by Inna Logunova.

30 Best Resources to Study Machine Learning
Banner that links to Serokell Shop. You can buy stylish FP T-shirts there!
More from Serokell
anomaly detection in MLanomaly detection in ML
17 Resources to learn Rust programming language17 Resources to learn Rust programming language
Interview with J. Johnson, Ph.D., “At the Intersection of Data Science and Biotech”Interview with J. Johnson, Ph.D., “At the Intersection of Data Science and Biotech”