Machine Learning: The New AI (The MIT Press Essential Knowledge series) Paperback – October 7, 2016

The author tackles machine learning from a very philosophical slant. I think this is a pretty strange approach for a subject that a very broad spectrum of people are curious about. On the other hand, if you have a fairly decent technical, mathematical and scientific background as I have, a fairly fluffy technical discussion with a good philosophical perspective is satisfactory. I think that writing a mass paperback on machine learning is a tough sell from the philosophical approach, which many (especially those who want to jump on the job bandwagon in data science) will certainly find unhelpful. I liked, actually, the fairly superficial discussions of numerous fairly up-to-date technologies in machine learning. I do not fool myself, however. It is also necessary to have at least some knowledge of the more technical, mathematical aspects, as in Alpaydin's machine learning text that this short paperback was, to some extent, drawing on. I recommend this book if you want a quick, superficial tour of relatively modern machine learning technology. It is obviously a book of no or little interest to the pro, undergrad major or grad student in related areas of data science. I suspect a fairly good audience would be freshman or high school students interested in machine learning.

A clear and concise overview of Machine Learning. I always wondered how AI could determine so many of our wants and desires before we knew them ourselves. Turns out it's all based on probability. What we want is based on how we match up with what others want. If a person likes or buys A, B, and C then we are offered what others desired after they also bought A, B and C. Now I know why some of the suggestions are spot on and others are completely off. The more you hew to the "normal" the better your recommendations will meet your expectations. Machine Learning tends to standardize our likes and dislikes, reinforcing our already formulated ideas.

Phenomenal book. The author does a great job of making complicated concepts simple. One criticism is the author writes a lot in passive voice. However, the book does a great job of explaining machine learning.

It is a good read for those who want to get a quick idea about all the attention that has given to machine learning and Al. But of course you will need more serious and in-depth books than this if you are serious about learning the topic.

The book was shipped right away and it is in good condition just like it was described. Thanks!

Very nice and gentle introduction into the field of Machine Learning. It will open up your mind and desire to get started into a more serious journey to learning about ML at a more deeper level. The book will not bore you with the mathematical foundations of ML, but it will leave you with the desire to get you to explore more about this interesting field where data, math and computing all come together. I really liked this book.

Important to know what the book is and what it is not. It is an excellent survey of the field of machine learning. It is not a "how to do machine learning" book. Anyone who has done or is interested in doing machine learning programing need not look here for instruction. For anyone who is interested in learning the terminology and scope of approaches that comprise machine learning, this book is current, concise, and informative.

I purchased this book thinking a book published by MIT would really be above other books on Machine Learning (ML); what it is and how it functions from a technical and theoretical perspective. My opinion is that this book misses the expectations that I set for it.- There is a lot of high level redundant wording throughout the book. In the first 3 chapters the author talks about some of the basic issues with ML from a statistics perspective on a some-what meaningless topic (the value of a used car). I gleaned nothing additional that made me question how to create a "used car" algorithm better.- This book hasn't enlightened me further that would allow me to consider what to do with data or how to build an algorithm, or how ML ties into Artificial intelligence (AI) or what AI is in relation to human intelligence. There is a little bit of information; however, it such a high level, if you bought this book to further your knowledge, you already what researchers are hypothesizing.- The date of publication for the book is 2016, which is only 4 years ago; however, on the first page of Chapter 3, the author states "recently two-dimensional matrix barcodes have been proposed..." Say what? Two-dimensional barcodes were registered in the late 1990's early 2000's and were being used by 2005. I'm not sure how an author writing a book in 2015/2016 could claim that two-dimension matrix barcodes are just being proposed, which then brings into question the current-ness of their information. This is a huge issue for me.- Although I am on Chapter 5 and not yet finished, I readily skipped paragraphs in the first 3 chapters because of the "basic" nature of the discussion in the paragraph. I found Chapter 4 on Deep Learning better; however, nothing that wow'ed me. You can find better and more thorough explanations on YouTube for Deep Learning.All in all, not a worthwhile investment to learning about ML.