{"id":24001,"date":"2020-02-19T08:00:00","date_gmt":"2020-02-19T16:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=24001"},"modified":"2020-02-20T08:07:53","modified_gmt":"2020-02-20T16:07:53","slug":"book-review-python-machine-learning-third-edition-by-sebastian-raschka-vahid-mirjalili","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/02\/19\/book-review-python-machine-learning-third-edition-by-sebastian-raschka-vahid-mirjalili\/","title":{"rendered":"Book Review: Python Machine Learning – Third Edition by Sebastian Raschka, Vahid Mirjalili"},"content":{"rendered":"\n
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I had been looking for a good book to recommend to my “Introduction to Data Science” classes at UCLA as a text to use once my class completes … sort of the next step after learning the basics. That’s why I was looking forward to reviewing the new 3rd edition of the widely acclaimed title “Python Machine Learning<\/a>” by Sebastian Raschka, Vahid Mirjalili. The book is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a useful resource you’ll keep coming back to as you fill up your data science toolbox.<\/p>\n\n\n\n

I knew I was going to like it the minute I started thumbing through the pages and saw some mathematics. I had been warning my students early on that eventually they’d have to break down and engage the mathematical foundations of machine learning to become a down-in-the-trenches data scientist, so this book fits that bill nicely. Many of the chapters start off with some theoretical aspects of the topic being discussed, including some math, followed by plenty of nicely written Python code. It should be noted that this book is not for beginners, and if you don’t know the Python language, you’ll have to find another learning resource before consuming this book.<\/p>\n\n\n\n

I appreciated Chapter 2, “Training Simple Machine Learning Algorithms for Classification” which goes all the way back to the beginning of machine learning and defines the “perceptron” algorithm (circa 1957 and Frank Rosenblatt’s seminal paper), and includes the code for implementing this simple model. I think it is a great learning experience to play around with this code to fully understand how this field got started. <\/p>\n\n\n\n

The balance of the chapters represent a tour de force of the field of machine learning, with few stones left unturned. Here is a list of topics covered in the book which should give you a good impression for the broad scope addressed for data scientists of varying levels of expertise:<\/p>\n\n\n\n