Machine Learning With R 2Ed (Pb 2015)

Its an awesome book on machine learning techniques. Explanation is very simple. The explanation on support vector machines was inadequate though.

Great book if you are new to Machine Learning and R. ML Concepts are excellently explained along with implementation in R

This is a fantastic book which explains how to implement various ML algorithms using. Every script in the book works like charm and it helped me understand some concepts of ML. The book is focused and appears to target people who want to work in ML without exposure to statistics and other fundamentals of ML. I would have been very happy if it had covered artificial neural networks in slightly more detail (how these networks are implemented) explaining the pseudo code. One of the biggest challenge in understanding ANN is how is it implemented. What flows from one layer to next. I would definitely recommend this book

The book is excellent

Very nice book.. Simple explanations with examples and use cases..

Timely delivery and product is as expected.

It is a decent book.it gives a good overview of machine learning.however it doesn't cover important packages like dplyr and data.table, ROC and concepts of logistic regression. It is a book suitable only for beginners

A concise book on ML but only covers the basics. Practical use of knowledge is quite limited but does produce a great interest to study the concepts in detail.

Provides a good top-level introduction to some of the common machine learning algorithms and how to apply them in R. I particularly appreciated the plain English writing style and mix of approaches used to explain the concepts. However, it doesn't go very deep into the theory so I would recommend it for those who are new to machine learning and/or R.

Very superficial information! The author hardly scratched the surface... a very disappointing book. I regret buying it.

This book has opened a new world for me! I bought it to get some understanding about machine learning. The book holds everything what it promises in the title. The author gives a very gentle introduction to key issues in statistics. Even simple things like the difference between mean and median are explained. But the book is also a crash course on R. Parallel to my reading I could experiment with the data and the R environment. Especially intriguing for me was that one could follow the data analysis hands-on with real data sets! (I didn’t know previously that there are real data sets free available on the internet – for instance at the UCI machine learning repository). And all this could be done without previous knowledge of R. I have to confess that some of the statistical details in the later chapters I didn’t understand completely in my first reading. But I didn’t expect that with my first dive into the domain of machine learning I will become a professional data scientist. I got some understanding about the main concepts and know now where to go for further practice and to build up my skills for analysing big data. The book is also (almost) perfect from an educational point of view. After two introductory chapters (one about general features of machine learning and one about the first steps and general syntax of R) the next seven chapters follow the same outline: (1) Providing a general understand of the algorithms with strength and weaknesses: Explaining the most important formulas and the effects demonstrating with some illustrative sample data. This provides you with a qualitative understanding of the method. (2) The chapter continues with a practical demonstration in the following order: (a) Collecting data: Where to get the data set, references and explaining the structure of the data. (b) Exploring and preparing the data. Every R-command to load the data, to transform etc. is explained and written down as code. The data and even these command are provided in a .zip archive at github. (c) Training the model on the data (d) Evaluating the model performance, looking for and discussing the false positives and false negatives including their effects in the real world. (e) Improving the performance of the model. (f) And finally a summary with lessons learned from this chapter. Like the first two chapters the structure of the last three chapters are different too: They are dedicates on strategies for evaluating and improving of model performances and some other specialised issues on machine learning. Some suggestions for the third edition of machine learning with R: I mentioned the word „almost perfect“: The only three things I was missing: (A) Please provide a section with exercises and solutions for the next edition! This would be very important for the transfer from understanding to applicable skills. (B) I would like to see one application in learning analytics with a real data set from the educational domain. (C) And last not least – there should be a new final chapter „Where to go from here now“. But all in all: One of the best tutorial books I have read!

I really like this book as I find it easy to follow along with. The explanation are clear and simple and you could easily adapt the ideas to your own work. At no point when using it did I work through a load of pages and then reflect thinking I've done xy and z there but in truth I haven't learnt anything. The teaching does make it stick. That said compared to some of the other ML books I have, this is much less maths based. And also you wont learn much code (or about the algorithms and models used) outside of load up a csv, type in the relevant model algorithm parameters, run some predictions and hey presto you have a model that works... not so simple in real life! Its definitely great for getting started but glosses over a lot of the important steps regarding data pre processing.

I have gone through 3-4 books so far to start with ML, and this has kept so far the best balance between explanations for newbies and technical depth. Every chapter is presented with entertaining and realistic examples. Right now is the book I'm recommending to all my colleagues about this subject.