Machine Learning With R 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.

I bought this book with some prior knowledge on Machine Learning. I would say that I am very pleased with my purchase. The only weakness of this book is that it mostly offers an intuitive explanation behind the algorithms, without going into much depth. Therefore, the book works extremely well for algorithms that make very small assumptions or whose procedures are simple: Naive Bayes, k-Nearest Neighbors, K-Means, Market Basket Analysis, etc. On the other hand, for more mathematically complicated procedures, like OLS Regression, Support Vector Machines, Neural Networks the explanation is poor because without some math it is really hard to perfectly convey what the model is doing.The examples are well thought and I like how the book works the examples from questions previously posed in the chapter.On the code front, the accompanying code is simple and the book works with datasets that are almost complete, clean, and without entry mistakes (something that NEVER happens in real life). Nevertheless, I believe that the code provided can be translated to real-world situations (I have done it myself for Naive Bayes and K-means, at least.).Things that could improve the book:1 - A mathematical appendix that fleshes out some more complicated algorithms.2 - Exercises at the end of each chapter.3 - More examples.Overall, I think the book is pretty good and enjoyable.

It's a good book to start with machine learning, easy to learn with examples. I am satisfied with this book. The only problem is: before I read this book, I thought I could learn almost everything I need for machine learning. However, after I finished this book, I discovered that the more I learn, the less I know.

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.

Overall liked this book a lot, and can quickly read through. Wish there would be a chapter talking about random forest.