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|Machine Learning in Action
MEAP Began: February 2011
Softbound print: February 2012 (est.) | 375 pages
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|Table of Contents||Resources|
|Part 1: Classification
1 Machine learning basics - FREE
2 Classifying with k-nearest neighbors -AVAILABLE
3 Splitting datasets one feature at a time: decision trees - AVAILABLE
4 Classifying with probability distributions: Naïve Bayes - AVAILABLE
5 Logistic regression - AVAILABLE
6 Support vector machines - AVAILABLE
7 Improving classification with a meta-algorithm: Adaboost - AVAILABLE
Part 2: Forecasting numeric values with regression
8 Predicting numeric values: regression -AVAILABLE
9 Tree-based regression - AVAILABLE
Part 3: Unsupervised learning
10 Grouping unlabeled items using k-means clustering - AVAILABLE
11 Association analysis with the Apriori algorithm -AVAILABLE
12 Efficiently finding frequent itemsets with FP-Growth - AVAILABLE
Part 4 Additional tools
13 Using principal components analysis to simplify our data - AVAILABLE
14 Simplifying data with the singular value decomposition - AVAILABLE
15 Big data and MapReduce - AVAILABLE
A Getting started with Python - AVAILABLE
B Linear algebra - AVAILABLE
C Probability refresher - AVAILABLE
D Getting reliable data
It's been said that data is the new "dirt"—the raw material from which and on which you build the structures of the modern world. And like dirt, data can seem like a limitless, undifferentiated mass. The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades.
"Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. By implementing the core algorithms of statistical data processing, data analysis, and data visualization as reusable computer code, you can scale your capacity for data analysis well beyond the capabilities of individual knowledge workers.
Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, 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.
As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.
Some programming background is helpful, but no prior knowledge of Python or machine learning techniques is required.
Peter Harrington holds Bachelors and Masters Degrees in Electrical Engineering. He worked for Intel Corporation for seven years in California and China. Peter holds five US patents and his work has been published in three academic journals. He is currently the chief scientist for Zillabyte Inc. Peter spends his free time competing in programming competitions, and building 3D printers.
This Early Access version of Machine Learning in Action enables you to receive new chapters as they are being written. You can also interact with the authors to ask questions, provide feedback and errata, and help shape the final manuscript on the Author Online