# Machine Learning in Action

*Machine Learning in Action* is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You’ll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

*Machine Learning in Action* is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you’ll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You’ll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

**What’s inside**

- A no-nonsense introduction
- Examples showing common ML tasks
- Everyday data analysis
- Implementing classic algorithms like Apriori and Adaboos

**Table of Contents**

Part I: Classification

Chapter 1. Machine learning basics

Chapter 2. Classifying with k-Nearest Neighbors

Chapter 3. Splitting datasets one feature at a time: decision trees

Chapter 4. Classifying with probability theory: naïve Bayes

Chapter 5. Logistic regression

Chapter 6. Support vector machines

Chapter 7. Improving classification with the AdaBoost meta-algorithm

Part II: Forecasting numeric values with regression

Chapter 8. Predicting numeric values: regression

Chapter 9. Tree-based regression

Part III: Unsupervised learning

Chapter 10. Grouping unlabeled items using k-means clustering

Chapter 11. Association analysis with the Apriori algorithm

Chapter 12. Efficiently finding frequent itemsets with FP-growth

Part IV: Additional tools

Chapter 13. Using principal component analysis to simplify data

Chapter 14. Simplifying data with the singular value decomposition

Chapter 15. Big data and MapReduce

Appendix A. Getting started with Python

Appendix B. Linear algebra

Appendix C. Probability refresher

Appendix D. Resources

### Book Details

**Paperback:**384 pages**Publisher:**Manning Publications (March 2012)**Language:**English**ISBN-10:**1617290181**ISBN-13:**978-1617290183