Python Machine Learning by Example - Third Edition: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn Paperback – 30 October 2020

Python Machine Learning By Example - The name of the book itself clears out what to expect from the book, which it doesn't disappoint. The book helps you solve everyday problems with ML before delving into advanced concepts.Anyone who watches Netflix and wonder about how the recommendation system works? The book will help you understand the core concepts necessary for how a recommendation system is built and not just that it will help you build one.Another set of common examples include Stock price Predictions and Recognizing faces, Classifying images.The book covers a wide range of concepts, from basic ones like Classification, Regression to more advanced ones like CNN and RNN.Overall, the book is very helpful with the examples and theoretical information provided, which makes it a perfect one for people who would be interested to learn, build ML applications and not get overwhelmed by the theoretical information.

Though I liked the book as it takes a balanced approach to a topic by including concepts and code, I was disappointed on reaching page 7790. The next page is 7821... 30 pages are missing from the Kindle version that I purchased

Has all the things you need to know.

I like the book but the typesetting of the book is very poor. I would prefer this book in color typeset

The concepts are explained clearly and step by step. Machine learning is not an easy topic, I find that this book is really helpful

I found that the example code to predict stock prices has issues. On page 249 the code calls out a variable y_test that is not ever generated for the stock price code, there are examples in between setting up the y_test variable but the values would not work with the stock price code. A little disappointed in this. I would love to know if I am wrong on this, I have gone through the pages over quite a few times to make sure I wasn't missing anything

If you are new to machine learning or Python, there are probably better resources. This book provides a helpful intro to key concepts, but does not cover mathematical topics in great detail. Also, many of the programs in the book do not work. You will also spend a LOT of time on errors versus learning machine learning - the data and code are old, so must be updated. Too much troubleshooting and editing than productively learning machine learning, in my humble opinion.