Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems, 2nd Edition

If you need a proper introduction to Machine Learning for professional reasons or even just for your own edification, do yourself a favor and pick up this gem of text. Make sure you are 'language agnostic' before you begin. Let me explain, right now the python libraries are all the rage: Pytorch, Keras, TensorFlow, ScikitLearn, etc... Thus, you might be tempted to believe that in getting yourself acquainted with ML in R you are putting yourself at a disadvantage. You'd be wrong. Truth it, you should be approaching the subject with the idea of learning from a conceptual and practical standpoint, albeit at a high level. The language you use will make little difference at the beginning. This was my main concern as I needed to learn "python ML" for professional reasons. Make no mistake, this book along with the available code up on the author's GitHub will guide you through the language, the hard to grasp concepts, and the terminology in a way that is pedagogically so effective that you'd be left wondering how it is that most technical books never reach this level of clarity. You'll be carrying conversations with experienced ML practitioners in no time, without embarrassing yourself (too much). Take it for what it is though, an introduction. If you need to know every pedantic detail about how neural networks learn, the heavy mathematical proofs behind the algorithms, etc., then you'd be much better served looking elsewhere. Once you go through this text, you'll be able to jump on the Python bandwagon all while avoiding the risk of having the language's technicalities distract you from the core concepts. Go for it, happy learning.

I use this book as a go-to manual that guides me step-by-step in implementing different machine learning techniques. It has a ton of ready code you can use in R. The author explains very well the logic of each technique and this is very helpful to decide what technique to use depending on the nature of the problem you explore. The book is non-mathematical but it contains references if you want to dig into the math behind the algorithms which I do every now and then even though I come from the humanities (sociology) and business (MBA). I think it is a great book, easy to understand and teaches a lot of very practical skills. I hope the author updates regularly the book with new editions as the techniques and the algorithms evolve over time.

Excellent book. It covers all you need to get started with a solid foundation in machine learning. I would highly recommend it to any programmer (or reasonably logically minded individual) who wants to get into machine learning. EDIT: After finishing the book, I'd still recommend it, and everything I wrote previously still applies. I am now actively using the knowledge I gained from this book on a project. However, I am reducing my rating to 4 stars because the publisher/editor is absolutely atrocious. I found at least a dozen minor errors (mostly typographical such as repeating a section of a sentence) that never should have made it past a simple proofreading.

I really liked this book, I've read about 70% and I feel that it is very well organized. I first tried reading Applied Predictive Modeling, but couldn't grasp the concepts, then I tried this book, and it made learning the concepts waaay more effective because of Lantz writing style and nice illustrations. Its given me a good high level understanding of the various machine learning algorithms and it has some good basic intro to R such as vectors, dataframes, and lists. The hands on exercises were super helpful in learning the concepts also.

Written in simple language that is easy to understand. For each algorithm, it demonstrates the ideas with a simple example and it explains the basic mathematics used in the algorithm. Of course if you want to get a Ph.D in machine learning, this is not the book for you. However, if you have good technical background in computer science and math, you will learn a lot on the modern machine algorithms.

Clearly and well written, with logical step-by-step introduction to the various topics, and good explanations of the relevant libraries, their strengths and weaknesses, and then useful application to specific examples.

I'm torn. There are some useful gems in this book, and for the most part, the presentation is simple, albeit a bit pedantic and cartoonish at times. If I was trying to get up to snuff on a new machine learning method, I might start here, since it *does* provide starter code for a variety of problems. That's quite handy. It doesn't, however, go into much depth at all on any one topic. You can't read this book and expect to know how to do any one of these methods well. Certainly, it's a tall order to ask any one book to cover all ML topics in depth, but any potential reader should be aware that this just skims the surface of a whole bunch of topics. On top of this, who in the world edited this book? Every other page has horrible typos, missing words, repeated sentences. These are not trivial errors either. This is a book about data analysis and yet the reported data are clearly wrong in places, e.g., a result is listed as .06 percent in one spot and then .0006 in another (p. 271). Basic subject-verb agreement errors riddle the text, e.g. "These output is shown as follows". Sometimes these are trivial errors, but other times you have absolutely no idea what the intended meaning is. I have about 100 pages more to read but I'm starting to wonder if I'm just wasting my time.

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.

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