Book Image

Python Machine Learning by Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning by Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
15
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16
Index

Mining the 20 Newsgroups Dataset with Text Analysis Techniques

In previous chapters, we went through a bunch of fundamental machine learning concepts and supervised learning algorithms. Starting from this chapter, as the second step of our learning journey, we will be covering in detail several important unsupervised learning algorithms and techniques. To make our journey more interesting, we will start with a natural language processing (NLP) problem— exploring newsgroups data. You will gain hands-on experience in working with text data, especially how to convert words and phrases into machine-readable values and how to clean up words with little meaning. We will also visualize text data by mapping it into a two-dimensional space in an unsupervised learning manner.

We will go into detail on each of the following topics:

  • NLP fundamentals and applications
  • Touring Python NLP libraries
  • Tokenization, stemming, and lemmatization
  • Getting...