Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms

I'm a fan of Heaton's books in general, they are excellent resources for software engineers looking to learn about AI and neural networks but this book seems somewhat less focused than his other works. It feels a bit like a hodgepodge of different things - a useful hodgepodge, but a hodgepodge nonetheless. I look forward to seeing the rest of the books in this series!

This is a very poorly written book. There doesn't seem to be any structure and barely any content.
The chapter are very short and 1/3 to 1/2 of the text introduce and summarize the chapter leaving only couple pages for the content itself.
The content itself is barely scratching the surface and is poorly written.
There are many great introductory books and online article on the topic. Save your money

I bought a kindle version, but returned it because math formulas are illegible.
I browsed webs and found the problem is well-known on kindle.
So I will not buy any books of kindle version more which include math formulas.

What a strange book. Reads like it was written in ten minutes.

I found this book to be an excellent introduction to machine learning and artificial intelligence. I was particularly impressed with the author's writing style which made the book very enjoyable to read. My only complaint would be that the latter chapters of the book contained less code. The initial chapters had plenty of code snippets to allow the reader to follow along and implement the algorithms but the latter chapters did not. His code is available online, so it wasn't a big deal but the book would be 5 stars if the latter chapters contained more code.

Got stuck with the normalization part. Explains things assuming someone already knows these concepts. Might as well look up the concepts and then google them because that is more clear.

Seriously! This was the 7th book on AI, Data Science, Machine Learning, Statistical Analysis I bought. This should have been the first book. Now that I have read this, I can go back to my previous 6 books and finally understand what the previous books were talking about!

I'm about half way into this book and Jeff explains every topic very clearly. So far it has been a book on explaining topics and how one will go about getting to a result, but does not show exactly how to get there. (no copy and paste code, but it's more powerful than a copy/paste scenario anyhow). I'm enjoying it very much.

This book generally does a good job of not assuming prior math / notation knowledge. The problem I have with most ai or game theory books is that they assume you have a math undergrad or grad degree. I come from an applied arts (design) background and this book was really helpful for getting my head around the basics of ai algorithms. Some of the explanations were lacking completeness and the author doesn't clearly tie the last two chapters to the rest of the book with concrete examples. There are some formatting issues and errors in the book. However, the way most of the concepts were explained led me to order the next two books in the series in hopes of getting a few more valuable nuggets of understanding that many other books have failed to provide.

Introductory level machine learning, but not explaining all materials. After reading this book, you might feel that it's too vague to implement (most of algorithms explained vaguely). No explanation about pros and cons of algorithms.