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 higherlevel 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 daytoday 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 higherlevel 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 nononsense 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

Machine learning basics

Classifying with kNearest 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

Predicting numeric values: regression

Treebased regression

Grouping unlabeled items using kmeans clustering

Association analysis with the Apriori algorithm

Efficiently finding frequent itemsets with FPgrowth

Using principal component analysis to simplify data

Simplifying data with the singular value decomposition

Big data and MapReduce