**MilanoR**, and kindly contributed to R-bloggers)

R machine learning essentials will be published soon. The target audience is readers wanting to quickly get familiar with machine learning. The only requirement is knowing a bit about data analysis and/or coding concepts.

This book is not just a tutorial. Its target is not teaching how to build very sophisticated machine learning solutions. It doesn’t even provide the reader with a detailed description of many techniques. R machine learning essentials is a path full of hands-on examples that makes the reader familiar with the fundamental machine learning concepts. In addition, it teaches the reader how to use some simple and powerful R tools like the data.table package. In the end, the reader will be able to face new machine learning challenges finding, applying, and evaluating new techniques.

The path starts showing business challenges requiring data-driven solutions, in such a way that the reader gets involved understanding the potentiality of machine learning. Then, the book explains why R is a good choice to build quick and powerful solutions. After a brief R tutorial, the book shows a quick example of data analysis and machine learning. Then, using another example, the book goes through the essential machine learning steps illustrating them in a result-oriented way. Now that the reader masters the essential concepts, the book shows an overview of the most important machine learning algorithms. In the last chapter, the book shows a practical business challenge and a powerful machine learning solution.

Perhaps you know R but you are new to machine learning. Or perhaps you know some machine learning techniques, but have never used R. Or maybe you are already familiar with both R and machine learning, but want a deeper understanding of the fundamental principles. In all the cases, the book will get you up and running quickly.

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**MilanoR**.

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