Machine Learning in Action (1 ed)

Summary

Machine Learning in Action is a 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.

About the Book

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 written tutorial for developers. It avoids academic language and takes you to the techniques you'll use in your daily 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.

• A no-nonsense introduction
• Examples showing common ML tasks
• Every day data analysis
• Implementing classic algorithms like Apriori and Adaboost

Table of Contents

1. PART 1 CLASSIFICATION
2. Machine learning basics
3. Classifying with k-Nearest Neighbors
4. Splitting datasets one feature at a time: decision trees
5. Classifying with probability theory: naïve Bayes
6. Logistic regression
7. Support vector machines
8. Improving classification with the AdaBoost meta algorithm

9. PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION

10. Predicting numeric values: regression
11. Tree-based regression

12. PART 3 UNSUPERVISED LEARNING

13. Grouping unlabeled items using k-means clustering
14. Association analysis with the Apriori algorithm
15. Efficiently finding frequent itemsets with FP-growth

16. PART 4 ADDITIONAL TOOLS

17. Using principal component analysis to simplify data
18. Simplifying data with the singular value decomposition
19. Big data and MapReduce