Overall this is a well-written book by experts. Most things are easy to follow (even if you have to Google a few formula derivations). This book does border on being too dense sometimes expecting a lot from the reader. I found the early chapters on regression easy to follow but the expectations on your memory are high. I do appreciate that they re-iterated some formulas multiple times so I didn t have to keep flipping around. The various plots and graphs helped clarify things quite a bit. If I could change something I would add a discussion of why various degrees of freedom are chosen for certain tests and some other basic explanations about why equations are setup the way they are.

Excellent book for a beginner. Great explanation of basic concepts with some good examples. Highly recommend for anyone curious about statistics and data science!

This is an outstanding introduction to statistical learning that requires no prior knowledge of calculus or linear algebra. It replaces mathematical rigor with intuitive descriptions of why and when each of the discussed methods work. The focus is on the process of learning from data using software libraries and about the strengths and limitations of each method. Implementation details of the methods proofs of correctness and rigorous mathematical analyses are simply left out. Readers looking for those details will be disappointed. However the presentation will be rewarding to anyone willing to accept statements of mathematical properties at face value.The R programming language is used to demonstrate methods and it is also the basis for all the hands-on exercises. The text assumes absolutely no prior knowledge of R nor does it pretend to teach people how to become R programmers. Instead there is enough material included to allow anyone with rudimentary programming experience to solve the exercise problems and gain some real experience with statistical learning.If you are a software developer looking to understand what the field is all about this is the best introduction to the subject I ve read. Even readers with sophisticated mathematical training will recognize how carefully the subject is developed for the non mathematical reader (the authors being amongst of the leading researchers in the field).

This book covers most of the primary techniques used in data science and machine learning. Each chapter is devoted to a topic and explained further throughout sections within the chapter.I don t quite have the mathematical foundation I need to get the most out of this book. For example in reading chapter 3 on linear regression I was following along just fine but once all the mathematical formulas got more complex page after page I was lost. I realized I don t have the proper grounding in match to follow along.If you re someone like me with a poor math foundation it will prove to be a difficult hurdle to overcome as you cover the book. (I believe they still offer a free PDF version on their site so take a quick perusal and if you find yourself crosseyed from the myriad of formulas presented then it s best to save it for a later purchase.)If you ve got strong math skills then this book will be a joy to read. I need someone more basic in terms of explaining not only the symbology used but how the formulas are derived.Hopefully I can find something more beginner and basic to guide me along so I can finally use this book for all it has to offer.

This book is indispensable for whoever wants to start with machine learning with solid foundations. It gets rid of the deep mathematical developments of The Elements of Statistical Learning (another great book) emphasizing the concepts and techniques. The labs and exercises in R are a superb addition giving you immediate hands-on practice as a start for doing your own exploration.The only negative aspect of this book is the total absence of references and bibliography. Not even the authors and researchers who have shaped the field are mentioned anywhere. The inclusion of a References section at the end of each chapter would have been very valuable for persons which want to go deeper in some topics and it wouldn t have increased significantly the length of the book..A great book overall.