Book: Hands-On Machine Learning with Scikit-Learn and TensorFlow

Someone remarked that the sign of intelligence is the ability to reduce complex subjects into simple straight-forward explanations.  This has been the best ML book I’ve encountered by that metric (and it’s only in Early Release form due in Feb 2017).

Chapter 2 is one of the best introductions to machine learning.  The book as a whole covers both the breath and depth of both traditional ML and Deep Neural Networks in a style unmatched by other ML books.

All the topics are well selected, sequenced and cover topics in a uniformly in-depth, lucid and practical hands-on style.  There are no weak or missing areas I could identify.  Even through this is an Early Release 4 months before official publication, I found no problems in the software or demos so far.

The book primarily assumes an undergraduate course in linear algebra and some familiarity with matrix-based programming using Python Scikit-Learn.  The linear algebra is essential to understand the concepts, however any matrix manipulation programming like matlab, R or Julia would make the programming implementations look very familiar.

Very highly recommended for those with Python scikit-learn and linear algebra backgrounds.  Even without these, there are large parts of the book that give excellent explanations to new learners.




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