An excellent introductory-intermediate book for any software engineer familiar with Python to quickly pick up machine learning. It will take the beginner to coding relatively proficiently with Python+Scikit-Learn and Tensorflow in a few weeks if you spend an hour or two a day.

The book provides a very nice end to end ML example right at the beginning (chapter 2) which helps to set the big picture, and the author then spend a chapter each on specific ML techniques such as Linear Regression, Logistic Regression, SVM, Decision Trees etc.

Appendix B – Machine Learning Project Checklist is particularly useful for those just starting out in Machine Learning and helps you to keep track of key tasks that you should be doing in the project.

Do note that the maths and statistics part is given a lighter treatment for the target audience in this book, so if you want deeper explanations, you have to refer to other texts. A newer second edition is due to be released soon based on Tensorflow 2.0.

This is a popular book within our team members and apprentices and hence I have decided to make this text as one the required reading for AI Singapore’s AI Apprenticeship Programme.