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Mastering Machine Learning with R

Mastering Machine Learning with R

By : Lesmeister
1.3 (3)
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Mastering Machine Learning with R

Mastering Machine Learning with R

1.3 (3)
By: Lesmeister

Overview of this book

Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work.
Table of Contents (16 chapters)
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Support vector machines

The first time I heard of support vector machines, I have to admit that I was scratching my head, thinking that this was some form of academic obfuscation or inside joke. However, my fair review of SVM has replaced this natural skepticism with a healthy respect for the technique.

SVMs have been shown to perform well in a variety of settings and are often considered one of the best out-of-the-box classifiers (James, G., 2013). To get a practical grasp of the subject, let's look at another simple visual example. In the following screenshot, you'll see that the classification task is linearly separable. However, the dotted line and solid line are just two among an infinite number of possible linear solutions.

You would have separating hyperplanes in a problem that has more than two dimensions:

So many solutions can be problematic for generalization...

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