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

How computers understand language – NLP

In Chapter 1, Getting Started with Machine Learning and Python, I mentioned that machine learning-driven programs or computers are good at discovering event patterns by processing and working with data. When the data is well structured or well defined, such as in a Microsoft Excel spreadsheet table or a relational database table, it is intuitively obvious why machine learning is better at dealing with it than humans. Computers read such data the same way as humans, for example, revenue: 5,000,000 as the revenue being 5 million and age: 30 as the age being 30; then computers crunch assorted data and generate insights in a faster way than humans. However, when the data is unstructured, such as words with which humans communicate, news articles, or someone's speech in French, it seems that computers cannot understand words as well as humans do (yet).