Packt Machine Learning with R - Second Edition Paperback – Big Book, 31 July 2015

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

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.

Ce livre est une véritable référence introductive dans la découverte des principaux modèles de machine learning avec R.Les explications sont simples, claires, précises et également très agréables à suivre.Pour ceux qui ne connaissent pas R un chapitre d'introduction lui est dédié permettant à tout un chacun d'appréhender aisément le langage de programmation.

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.

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