Book Image

Machine Learning with R Quick Start Guide

By : Iván Pastor Sanz
Book Image

Machine Learning with R Quick Start Guide

By: Iván Pastor Sanz

Overview of this book

Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.
Table of Contents (9 chapters)

Summary

In this chapter, you learned some introductory concepts of text-mining and topic extraction. You should now know how to read text files and process raw text to obtain useful common words. Also, you are now able to use the information collected in a text format in your own problems.

Depending on the amount of data and the type of problem you want to solve, you could now apply a variety of techniques, both simple and complex, used previously in this book.

Finally, and taking into account this chapter, you are ready to dive into other more recent and promising techniques, such as word2vec and doc2vec, which are both advanced techniques that allow you to discover relevant information or topics in a piece of text and documents. If you're curious, you can research these topics further.

I hope you got an in-depth view of machine learning and that this book has helped...