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

Data preprocessing and feature engineering

Data mining, a buzzword in the 1990s, is the predecessor of data science (the science of data). One of the methodologies popular in the data mining community is called the Cross-Industry Standard Process for Data Mining (CRISP-DM) (https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining). CRISP-DM was created in 1996, and machine learning basically inherits its phases and general framework.

CRISP-DM consists of the following phases, which aren't mutually exclusive and can occur in parallel:

Business understanding: This phase is often taken care of by specialized domain experts. Usually, we have a businessperson formulate a business problem, such as selling more units of a certain product.
Data understanding: This is also a phase that may require input from domain experts; however, often a technical specialist needs to get involved more than in the business understanding phase. The...