Machine Learning in Action

Machine Learning in Action, Free Ebook Download

Book Detail 

Paperback: 384 pages

Publisher: Manning Publications (April 16, 2012)

Language: English

ISBN-10: 1617290181

ISBN-13: 978-1617290183

File Size : 8.15 Mb | File Format : PDF

Book Description 

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.

Purchase includes free PDF, ePub, and Kindle eBooks downloadable at manning.com.

What's Inside

• A no-nonsense introduction
• Examples showing common ML tasks
• Everyday data analysis
• Implementing classic algorithms like Apriori and Adaboos

===================================

Table of Contents

PART 1 CLASSIFICATION
01. Machine learning basics
02. Classifying with k-Nearest Neighbors
03. Splitting datasets one feature at a time: decision trees
04. Classifying with probability theory: naïve Bayes
05. Logistic regression
06. Support vector machines
07. Improving classification with the AdaBoost meta algorithm

PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
08. Predicting numeric values: regression
09. Tree-based regression

PART 3 UNSUPERVISED LEARNING
10. Grouping unlabeled items using k-means clustering
11. Association analysis with the Apriori algorithm
12. Efficiently finding frequent itemsets with FP-growth

PART 4 ADDITIONAL TOOLS
13. Using principal component analysis to simplify data
14. Simplifying data with the singular value decomposition
15. Big data and MapReduce

Download Ebook : Machine Learning in Action

Rapid : Machine Learning in Action
Ziddu : Machine Learning in Action