You're reading from Scikit-learn Cookbook Over 80 recipes for machine learning in Python with scikit-learn

Product type Paperback

Published in Dec 2025

Last Updated in Sep 2025

Publisher Packt

ISBN-13 9781836644453

Length 414 pages

Edition 3rd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

John Sukup

View More author details

Table of Contents (14) Chapters

1. Scikit-learn Cookbook, Third Edition: Over 80 recipes for machine learning in Python with scikit-learn

2. Chapter 1: Common Conventions and API Elements of scikit-learn FREE CHAPTER

3. Chapter 2: Pre-Model Workflow and Data Preprocessing

4. Chapter 3: Dimensionality Reduction Techniques

5. Chapter 4: Building Models with Distance Metrics and Nearest Neighbors

6. Chapter 5: Linear Models and Regularization

7. Chapter 6: Advanced Logistic Regression and Extensions

8. Chapter 7: Support Vector Machines and Kernel Methods

9. Chapter 8: Tree-Based Algorithms and Ensemble Methods

10. Chapter 9: Text Processing and Multiclass Classification

11. Chapter 10: Clustering Techniques

12. Chapter 11: Novelty and Outlier Detection

13. Chapter 12: Cross-Validation and Model Evaluation Techniques

14. Chapter 13: Deploying scikit-learn Models in Production

Introduction to Text Processing

Text processing is a fundamental step in ML, especially crucial when dealing with natural language data. It is estimated that somewhere between 80% to 90% of data is unstructured data which includes text as well as other non-traditional data sources like images, video, audio, and so on. Like structured data, textual data often contains noise, irrelevant information, and varying formatting that can pose challenges for effective modeling. Effective preprocessing converts raw text into structured numerical data, enabling the application of machine learning algorithms. Let’s start by learning some of the basic scikit-learn tools for working with this type of data. We will also incorporate a few other Python libraries including Pandas and numPy.

Getting ready

We'll prepare our environment by loading essential libraries and text data. Now that we are using text data rather than numeric data, we will utilize a built-in dataset from Python’s...