# Machine Learning with R – Book Review

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I have to admit it…I’m an R junkie…since the first time I started learning R…it has become an addiction to me…I try to solve every problem with R…which is obviously not the best way to go…but my rule of thumb is…”Try with R first…otherwise…just use something else and then go back to R” -;)

This time I was really excited to read a new R book (well…maybe not new…but new for me) called Machine Learning with R…

For me this book should be called “The Big Book for R nerds”…with 396 pages…this book is just beautiful, amazing and one of the best R books I have ever read…

Of course…keep in mind that is not a book for beginners…you need to have previous R experience to fully understand everything…so if you’re not a R advocate…please help yourself and read the also awesome The R Inferno…

The book of course, contains a small introduction to R principles and most used commands like Vectors, Factors, Lists, Data Frames and data manipulation.

When the book really gets interesting is when the Machine Learning gets introduced…

It a nutshell…a Machine Learning algorithm will take input…learn something and the toss out a result that will help us make a decision…simply pure magic -:)

Some of the algorithms covered in this book…and covered in a really easy and digestible way with some of the best examples you could think of…are these…

Nearest Neighbor

naive Bayes

Decision Trees

Classification Rule Learners

Linear Regression

Regression Trees

Model Trees

Neural Networks

Support Vector Machines

Association Rules

k-means Clustering

A little bit overwhelming, huh? Well…not really…R and it’s plethora of packages makes your life easier….after reading this book…you will be able to apply each and everything single algorithm to your real life projects…but of course…you experience, trial and error and perseverance will be highly appreciated…

Let’s see some examples…

**Cross table for a Nearest Neighbor**

**Decision Tree**

**Neural Network Diagram**

**Association Rules**

**Random Forests**

- Evaluation Model Performance
- Improving Model Performance
- Specialized Machine Learning Topics

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