Machine Learning

It's a Good and renowned Book for Beginners who want to learn ML. The basics are explained well, along with the mathematical details. I paid eight-twenty-eight, including delivery charges, for this book. Its Publisher is AFFILIATED EAST-WEST PRESS. Not the McGraw-Hill Press. On time delivery. Black and white printing and the Quality of Printing are good.

Seller seems to be good. Book is in excellent condition.

PDF is free online but hard copies are always welcome. Good book to get started on ML as well. A little too much if you don't have a good math or CS background but otherwise for people working already in the industry, this can be used as a go to book to clear any doubts.

Good Book

Paper quality is poor

This was my first machine learning text book after Andrew Ng course .the book provides good introductory machine learning algorithm along with proof like gradient descent, maximum likelihood principle which I found very useful along with pseudo code .definitely recommended to kick-start your journey as it is short book with less math and more intuition behind the algorithm which is very useful to get a foundation for further study.

The book covers a wide range of topics It is a very good book for an overall idea of the subject and for an introductory course. The advantage is that each chapter has a section on "Further Reading" which has lists of more advanced material on each topic for those who want to learn the subject thoroughly and mathematically. Overall a very good book

I am new to Machine Learning and this is my first book(read 4 chapters of Ethem Alpaydin; found that good as well). Have some exposure to Fuzzy Logic, Neural Networks but otherwise not much. I find this book very easy and have covered till 6th chapter now(in 3 weeks). I can confidently say, I follow almost 85% of the content that I have read so far. But without a good grounding in set notations, probability, logic etc one could find it not that easy to turn pages. Obviously doesnt cover latest developments as the original text was published in 1997. Nevertheless looks and feels very relevant since it provides a framework to think about and analyze machine learning algorithms.

The only down side to this book is the print size otherwise it is very detailed.

Good book, I am happy with my purchase. Overall condition is excellent!

Everything was as the description mentioned. PS.: There are some pages with letters missing, like the printer had a dried toner.

This book is a great starting point for machine learning. It's not directed towards application, it's more theory driven. If you're uncomfortable with symbolic logic you will struggle with this book. If you don't know symbolic logic I'd suggest a textbook in discrete mathematics before diving into this book. It would also help to know some linear algebra or set theory. If you have at least a Bachelors in Mathematics you'll be able to follow this book easily. That said, this book will help you become a producer of machine learning rather than a consumer of some out-of-the-box ML package.

Book arrived in good condition.