Real-World Machine Learning

This book addresses all the real-life issues in machine learning. Highly readable and practical book!

Not recommended. Waste of money.

Can see the examples everywhere. No need to buy this book to see the examples.

While this is a practitioner oriented book, it will be useful to anyone who is learning about machine learning. This book does a very good job of illustrating the "how" of machine learning--- the practical steps of organizing data sets, and the various steps involved in building up and evaluating models.The book is very well organized, and well presented. The authors crafted a good systematic approach for explaining and illustrating through example the various steps of the process of using ML methods to create models for classification and prediction. They book has a number of good examples.No mathematical background is required for reading this book. Obviously it helps if the reader has some familiarity with the various types of statistical models used in ML. Even if that is not the case, the book is a good starting point for bridging between the "what" of ML and the "how" of ML. For those who want to try things in a hands-on fashion, they give a number of code examples, with sufficient brief annotations so you know what the blocks are code are being used for.

This is one of the very few "hands on / applied / real-world" ML books.Highlights:Page 11: I strongly believe that any ML practitioner needs a work-flow/checklist. You rarely (never?) see that with academic books.Page 19: Loved the visual display of features to target (class). Yes, it's simple. Yes, it's non academic. But it's extremely useful for my ML mind to think visually like that.Page 33: How much training data is required? Three cases.Page 44: Which visualization tool do you need if your input feature is categorical and your response feature is numerical? Awesome matrix!Page 113: Wow! Feature Engineering! How to convert a single feature (time-stamp) into a 10 datatime feature.Page 137: Love the notes "within" the Python code. The publisher Manning seems to push that ....Great work!

There are two situations where I write reviews:1) when something really sucks2) when something is really goodI took so many machine learning classes online taught by PHDs. I got lost way too many times.This book explains everything so well even to someone who is not very academic like me. Highly recommend it.One of the best books I've read, and the definitely THE best book I've read on machine learning.

Price is high