You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Summary

In this chapter, you learned the fundamental concepts of NLP as an important subfield in machine learning, including tokenization, stemming and lemmatization, and PoS tagging. We also explored three powerful NLP packages and worked on some common tasks using NLTK and spaCy. Then, we continued with the main project exploring newsgroups data. We began by extracting features with tokenization techniques and went through text preprocessing, stop word removal, and stemming and lemmatization. We then performed dimensionality reduction and visualization with t-SNE and proved that count vectorization is a good representation for text data.

We had some fun mining the newsgroups data using dimensionality reduction as an unsupervised approach. Moving forward, in the next chapter, we'll be continuing our unsupervised learning journey, specifically looking at topic modeling and clustering.