Machine Learning For Absolute Beginners: A Plain English Introduction: 3 (Data Storytelling: Learn AI, Data Science & Python Books for Beginners) Paperback – 1 Jan. 2018

Very logically structured and with clear chapters which hang together (about 5-10 pages long). It covers the components/decisions to make in Machine learning, data preparation, the main algorithm methods, the development environment and making a model in python. Don't be put off by the python you can read 90% of the book not knowing anything about Python.Great value for moneyI do have a background in stats and algorithms so even though the language is clear some of the underlying concepts do take a bit of thinking about.A suggestion if you cannot take the algorithms on board is to get 20 pictures of dinosaurs and try putting them into 2 categories using each of the "unsupervised" algorithms that the author describes - for the supervised algorithms get 20 pictures of cats/dogs and correctly label half of them (the "training set") as "Cat" or "Dog" leaving the other half without labels (to use either as "test" data or "actual" data)...

It’s a good primer for any beginner who is curious about the field. Rather than just being an overview of the topic it gives some basic practical knowledge to make a start with machine learning. Losing a star due to being a very poor print. Mine had part of another book printed half way though not sure how that would even happen!

This little introduction to Machine Learning is a gem. It is for the absolute beginner as it explains in the title. But it acts as an extremely useful helicopter view of the subject, not in journalist style, but actually very true to the mathematics and methodologies underlying the techniques. The book also helps those thinking about a career in machine learning. They will get an overview of what it is about and what types of skills are required.For those with a deeper mathematical maturity, this book could also act as a good reference framework for all the different approaches and techniques covered. For example, to understand what reinforcement learning is (and why it is so difficult) or ensemble learning techniques. The real difference between standard regression and logistic regression. Traditional textbooks and monographs can become overburdened in the technical mathematics and make the reader lost in terms of where they are in the whole overall scheme of machine learning.

The book is ok for review of basic AIML. I found the book bit more technical for beginners as opposed to focusing on the basics.

I think this book does a really good job at explaining all the basic concepts of machine learning. I had started a Matlab course on ML at work for personal development but to be honest I hadn't taken the time to get an overview of the basic concepts written in clear understandable language. This does the job quite nicely. Some comments say that you can get all the information online... you could make thag comment about any knowledge base these days but thus is a well structured plain english book on machine learning that a pure beginner can sink their teeth into.

I wanted to know where to start in learning computer programming, and I think this book is a good beginning for anybody with very little knowledge.

I was looking for a starter book , considering I had zero knowledge of ML.This is absolutely perfect. The 15 chapters are each roughly 8 to 10 pages and can easily be read with a coffee in about 20 to 25 minutes.After 3 days, I am 5 chapters in and already have some of the large concepts and have a very brief idea of some ot the current buzzwords in MLI'd fully recommend this book for people like me who are new to ML.Darren

I have just finished reading this excellent introduction to the topic of Machine Learning. It has been a long time since I have read any book in a single four hour sitting, with my brain so alight with the possibilities implied by what I’m reading about.