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.
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 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

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Table of Contents

PART 1 CLASSIFICATION
Machine learning basics
Classifying with k-Nearest Neighbors
Splitting datasets one feature at a time: decision trees
Classifying with probability theory: naïve Bayes
Logistic regression
Support vector machines
Improving classification with the AdaBoost meta algorithm
PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
Predicting numeric values: regression
Tree-based regression
PART 3 UNSUPERVISED LEARNING
Grouping unlabeled items using k-means clustering
Association analysis with the Apriori algorithm
Efficiently finding frequent itemsets with FP-growth
PART 4 ADDITIONAL TOOLS
Using principal component analysis to simplify data
Simplifying data with the singular value decomposition
Big data and MapReduce