After college I went to work for Intel in California and mainland China. Originally my plan was to go back to grad school after two years, but time flies when you are having fun, and two years turned into six. I realized I had to go back at that point, and I didn’t want to do night school or online learning, I wanted to sit on campus and soak up everything a university has to offer. The best part of college is not the classes you take or research you do, but the peripheral things: meeting people, going to seminars, joining organizations, dropping in on classes, and learning what you don’t know.

Summary

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

PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION

PART 3 UNSUPERVISED LEARNING

PART 4 ADDITIONAL TOOLS

1. Machine learning basics
2. Classifying with k-Nearest Neighbors
3. Splitting datasets one feature at a time: decision trees
4. Classifying with probability theory: naïve Bayes
5. Logistic regression
6. Support vector machines
7. Improving classification with the AdaBoost meta algorithm
8. Predicting numeric values: regression
9. Tree-based regression
10. Grouping unlabeled items using k-means clustering
11. Association analysis with the Apriori algorithm
12. Efficiently finding frequent itemsets with FP-growth
13. Using principal component analysis to simplify data
14. Simplifying data with the singular value decomposition
15. Big data and MapReduce