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Is this book for you? You will enjoy the book if you are: - a software engineer or a scientist who wants to become a machine learning engineer or a data scientist - a data scientist trying to stay on the edge of the state-of-the-art and deepen their ML expertise - a manager who wants to feel confident while talking about AI with engineers and product people - a curious person looking to find out how machine learning works and maybe build something new Preface Chapter 1: Introduction Part I: Supervised Learning Chapter 2: Notation and Definitions Chapter 3: Fundamental Algorithms Chapter 4: Anatomy of a Learning Algorithm Chapter 5: Basic Practice Chapter 6: Neural Networks and Deep Learning Chapter 7: Problems and Solutions Chapter 8: Advanced Practice Part II: Unsupervised and Other Forms of Learning Chapter 9: Unsupervised Learning Chapter 10: Other Forms of Learning Chapter 11: Conclusion The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics - both theory and practice - that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field