Skip to content

Machine Learning Resources

Machine Learning community is very vibrant. Complete Machine Learning Package can get you started, but it's not enough. Fortunately, there are many faboulous learning resources, some of which are paid or freely available. Here is a list of courses that are often recommended by many people. Note that they are not listed in an order they are to be taken.


  • Machine Learning by Coursera: This course was taught by Andrew Ng. It is one of the most popular machine learning courses, it has been taken by over 4M of people. The course focuses more about the fundamentals of machine learning techniques and algorithms. It is free on Coursera.

  • Deep Learning Specialization: Also tought by Andrew Ng., Deep Learning Specialization is also a foundations based course. It teaches a decent foundations of major deep learning architectures such as convolutional neural networks and recurrent neural networks. The full course can be audited on Coursera, or watch freely on Youtube.

  • MIT Intro to Deep Learning: This course provide the foundations of deep learning in resonably short period of time. Each lecture is one hour or less, but the materials are still the best in classs. Check the course page here, and lecture videos here.

  • NYU Deep Learning Spring 2021: Taught at NYU by Yann LeCun, Alfredo Canziani, this course is one of the most creative courses out there. The materials are presented in amazing way. Check the lecture videos here, and the course repo here.

  • CS231N: Convolutional Neural Networks for Visual Recognition by Stanford: CS231N is one of the best deep learning and computer vision courses. The 2017 version was taught by Fei-Fei Li, Justin Johnson and Serena Yeung. The 2016 version was taught by Fei-Fei, Johnson and Andrej Karpathy. See 2017 lecture videos here, and other materials here.

  • CS224N: Natural Language Processing with Deep Learning by Stanford: If you are interested in Natural Language Processing, this is a great course to take. It is taught by Christopher Manning, one of the world class NLP stars. See the lecture videos here.

  • Practical Deep Learning for Coders by This is also an intensive deep learning course that cover the whole spectrum of deep learning architectures and techniques. The lecture videos and other resources such as notebooks are the course page.

  • Machine Learning Engineering for Production (MLOps) Specialization: Taught by Andrew Ng., Laurence Moroney, and Robert Crowe, this is one of the best ML engineering course. It teaches how to design end to end machine learning production systems, building efficient data and modelling pipelines, and deploying models in production. You can find the course on Coursera and other course materials on Github.

  • Full Stack Deep Learning: While the majority of machine learning courses focuses on modelling, this course focuses on shipping machine learning systems. It teaches how to design machine learning projects, data management(storage, access, processing, versioning, and labeling), training, debugging, and deploying machine learning models. See 2021 version here and 2019 here. You can also skim through the project showcases to see the kind of the courses outcomes through learners projects.


Below is of the most awesome machine learning books.

  • The Hundred-Page Machine Learning Book: Authored by Andriy Burkov, this is one of the shortest but concise and well written book that you will ever find on the internet. You can read the book for free here.

  • Machine Learning Engineering: Also authored by Andriy Burkov, this is another great machine learning book that uncover each step of machine learning workflow, from data collection, model serving and maintenance. The book is also free here.

  • Machine Learning Yearning: Authored by Andrew Ng., the book contains various strategies for building effective learning systems. It is partitioned into small parts which makes it easy to read and it is not sorely for machine learning engineers. Anyone working with data science and machine learning teams can find the book helpful. The official book is available here for free, but you can read or download it here without requiring to sign up.

  • Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Authored by Aurelion Geron, this is one of the best machine learning books. It is clearly written and full of ideas and best practices. You can get the book here, or see its repository here.

  • Deep Learning: Authored by 3 deep learning legends, Ian Goodfellow and Yoshua Bengio and Aaron Courville, this is one of the great deep learning books that is freely available. You can get it here.

  • Deep Learning with Python: Authored by Francois Chollet, The Keras designer, this is a very comprehensive deep learning book. You can get the book here, and the book repo here.

  • Dive into Deep Learning: This is also a great deep learning book that is freely available. The book uses both PyTorch and TensorFlow. You can read the entire book here.

  • Neural Networks and Deep Learning: This is also another great deep learning online book by Michael Nielsen. You can read the entire book here.

If you are interested in more machine learning and deep learning resources, you can find them here, here and here.

Back to top