Monica Kulkarni | |
A beautifully written and composed survey of modern statistical learning techniques. Nearly nothing of import is absent but full appreciation of the material probably requires basic basic science competence. Moreover each chapter ends with wonderfully constructed labs that will serve all R programmers as an essential playbook. Every scientist data scientist and data engineer should have and read this book. |
Schmander | |
Best introductory machine learning book in the history of mankind. Goes to the point and is simple and does not have big buzz words like many other books (and most of the people) in this field. |
David | |
For a technical book to be rated this high one has to stop and think why.This book is well-written concepts are clearly explained and still has a compact presentation.I applaud the authors for this!This book will open the mind of anyone looking for a gentle introduction to machine learning. |
NorthernVirginia1982 | |
As an undergrad just graduated with a OR degree I think this is a great and easy read. It s doesn t require linear algebra or strong statistics background but however it would you help if you know the basic concepts. The concepts are explained clearly. I would imagine someoen with a non-engineering or non-math background will still be able to grasp most of the concept. The labs in R are also very helpful. Great book overall. |
Sailaja Katta | |
A well-written text on statistical learning that introduces the key concepts in an easily accessible fashion. While this might not be an ideal text for a graduate level course (look up Elements of Statistical Learning by the same authors for deeper and more involved discussions) this is perfect for a beginner who wants to understand the core ideas of machine learning such as regression bootstrap etc. Also the R programming labs at the end of each chapter help the reader work through some examples and provide some starter code to get your hands dirty.I would highly recommend this book as a good supplement to standard machine learning texts such as Elements of Statistical Learning by the same authors PRML by Bishop |