Machine Learning For Absolute Beginners: A Plain English Introduction (Data Storytelling: Learn AI, Data Science & Python Books for Beginners) Paperback – January 1, 2018

I found this book to be a great resource for reference, although I'm not sure it's for people completely lacking statistical or machine learning knowledge. The author references ML terms quite early on without really explaining what they are.However, in my case, I was using this book to jog my memory of concepts I'd learned but forgotten in college. I majored in stats, with a focus in ML, then spent 2 years doing front end development. Now I'm going back to machine learning, and this book is the perfect, short and sweet summary of the terms and concepts needed to understand everything at a high level.It's a great reference to have on your bookshelf - especially to remind yourself quickly or a term, or if you're trying to compare and decide on what algorithm you need to use for a new dataset.

I want to say that this book is the perfect size at around ~150 pages if you exclude the exercises at the back. In my opinion, It's balanced with text and figures/graphics and provides a high level overview of concepts, models, algorithms, etc.The book itself is a fairly quick read and can be tackled in an afternoon if it keeps your interest. There are a wealth of additional resources the book provides in both footnotes and appendices. It doesn't waste it's time with understanding the fundamental mathematics and instead leverages the use of publicly available Python libraries. The math of Machine Learning can easily be intimidating and I commend the authors from shying away from it.

Happy to offer this book a glowing endorsement to beginners. Nothing in this book is intimidating. I was worried that I might get lost halfway through after the author started introducing the various algorithms. But no, this is where the book got significantly better. The visual examples and clear instructions made the rest of the book a breeze to follow. Although it was easy to follow, I don’t feel like I lost out on technical content. For a high-level introduction, it was concise and detailed.In terms of constructive feedback to the author, I offer the following suggestions: - Extend the chapter on ANN. I would have liked to have seen some full examples as with the previous algorithm chapters.- No mention of face recognition? This is an aspect of ML I think a lot of readers will be interested in discovering more. This I assume would slot into the chapter with ANN.

Judging this book by the cover, I had expected a good read. Upon seeing the book and flipping through, I felt a little letdown because I could see that it was written in MS Word, Arial font, and had a few irritating things left at default settings. Once I got into the subject matter, I easily looked past formatting issues and really enjoyed the material. Very interesting work and a great place for someone like me who has little to no clue what machine learning is to start.

I have been reading some ML books. This is one of my favorites. It is clear, concise. A great source for beginners.There are many terms discussed, also, some exercises.I wish there could be more exercises, but it is a nice step to start.There are many recommendations at the end of the book. I recommend that if you want some good base in ML, take a course, and use these books as a reference or to clear some topics. A must read for a beginner!

Attention to those who might pick up this book without having a strong background in data science or mathematics--large chunks of this book will probably be a slow slog for you, as it was for me.I am very surprised this book is rated so highly. There are so many concepts/things thrown out there as if they were common knowledge, when they're not. By the point the book starts diving into coding basics around Chapter 13, I realized I needed to find another, better book. I've found free online resources and an online course that have made Python much more digestible.

It serves its purpose, this book is for absolute beginners. You can read it in a day to understand what ML is but that is all it is.

This book was written in plain English and really meant for people without a lot of Math background to understand. Unlike other books such as "the 100 page ML book" which is full of math, this book explains the concepts very well, without using a whole lot of math. This book is NOT for those who are serious about digging into data science or machine learning. But it's good for everyone else who wants to get a high level overview of what ML/Data science is about.