This week, the post is an interview with Michele Usuelli. Michele is the author of the book "R Machine Learning Essentials".

Hi, Michele. Welcome back to MilanoR. You're the second author of this blog, after Max Marchi, who wrote a book about R. How has this idea started?

Everything started when Pack Publishing contacted me on LinkedIn out of the blue. First, they proposed me to write a book about a well-know R tool to build charts: the ggplot2 package. Since I was more interested in writing about Machine Learning techniques rather than an R package, I didn't follow up. When a few weeks later they proposed me to write a book about R and Machine Learning, I was really enthusiast and it took just a few days to made up my mind and start writing.


You've written this book by yourself. How was your experience as an author?

Writing this book involved a lot of challenges: decomposing the fundamental Machine Learning concepts, explaining them clearly, using a good writing style, managing my time. Each of these challenges allowed me to learn new skills and to grow. Timewise speaking, it took about 10 hours per week for 5 months, mostly during the summer. I recommend to work on such a big project in the winter, when there are less things going on :)


Let’s get into the book. What kind of knowledge is expected from the audience? Should readers be a bit familiar with R? What about IT knowledge?

It'll definitely be helpful to be a little bit familiar with programming and/or statistics. However, no prior experience is required. The book starts from scratch explaining the ideas behind Machine Learning. Then, it leads the reader through a path rather than just explaining concepts. The more familiarity has the author, the faster it will be to go through the path.


If you had to choose an example from your book, which code chunk would you share with the readers of this blog?

I'd choose the last chapter since it displays a nice practical application. It is business-driven and at the same time it shows different branches of Machine Learning. The business challenge is planning a marketing campaign. In order to determine which customers will be more likely to subscribe, the book performs Supervised Learning on the data of a past campaign. In additions, it applies Unsupervised Learning techniques to explore the customer base.


Is there any suggestion you’d like to give to someone who wants to write a book about R?

The outcomes of writing a book has paid off the effort. Plus, R has recently become a hot topic, so there are a lot of interesting unexplored topics. Don't be worry in investing a part of your time in a similar project. Also, try to focus on the skills that you can improve rather than just on the final product.

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