Recommended Books for Machine Learning

The following are a few of my favorite books on machine learning. These books range from pure theory to hands-on practical Python programming help. They are arranged roughly in order if you are starting your journey into ML, but can also be read out of order.

Stack of machine learning books
Some of my favorite books on machine learning
(click to enlarge).

  1. The Hundred-Page Machine Learning Book
    by Andriy Burkov
    This book is actually a bit more than 100 pages, but still slim enough to be read over a weekend. Burkov gives a nice overview of some of the problems, pitfalls and methods of the field. If you are interested in machine learning — even if have no desire to write ML code — this is a great place to start.
  2. The StatQuest Illustrated Guide to Machine Learning
    by Josh Starmer, Ph.D.
    This book goes into more depth with methods and models than The Hundred-Page Machine Learning Book , but it's still very accessible. Josh Starmer has a fantastic YouTube channel that I watch often and have assigned in courses as additional resources. Josh Starmer now works at Lightning AI.
  3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
    by Aurélien Géron
    I have the second edition of this book, but there is a newer edition of that also looks like a great resource. I thought this book handled scikit-learn exceptionally well, and the demise of tensorflow is greatly exaggerated. Tensorflow is still used by a large percentage of the machine learning community, with more users than ever before.
  4. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
    by Sebastian Raschka
    This is a great book by one of the lead developers for PyTorch. It is a large resource, with a ton of information. Page per dollar value is high!
  5. Machine Learning Engineering
    by Andriy Burkov
    This book provides a nice overview of what putting ML models into production actually means, and does a good job of describing the general workflow. It also points out pitfalls and other things to consider when building an ML model. It is not a hands-on book with code examples.
Bonus Book!
  1. Effective Pandas
    by Matt Harrison
    This is the book for learning intermediate and advanced pandas. While this is not a machine learning book, great pandas skills are key for dataset processing, exploration, and plotting.

Do you have some other favorite machine learning and data science books? Please mention them in the comments below!

Thomas Martin is an AI/ML Software Engineer at the Unidata Program Center. Have questions? Contact support-ml@unidata.ucar.edu or book an office hours meeting with Thomas on his Calendar.

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