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Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in
numerous academic journals.
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A no-nonsense introduction
Examples showing common ML tasks
Everyday data analysis
Implementing classic algorithms like Apriori and Adaboost
Machine Learning In ActionMachine 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.
Table of ContentsTable of Contents
Author: Peter Harrington
About the Author About the Author
ISBN: 978-93-5004-413-1
Pages: 380 | Price: ` 599/-
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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
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Predicting numeric values: regression
Tree-based regression
Grouping unlabeled items using k-means clustering
Association analysis with the Apriori algorithm
Efficiently finding frequent itemsets with FP-growth
Using principal component analysis to simplify data
Simplifying data with the singular value decomposition
Big data and MapReduce
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Inside the Book
