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Machine learning in action

Author: Peter Harrington
Publisher: Shelter Island, NY : Manning Publications, ©2012.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:
"Machine Learning in Action" blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. The author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features.
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Genre/Form: Handbooks and manuals
Handbooks, manuals, etc
Manuales
Document Type: Book
All Authors / Contributors: Peter Harrington
ISBN: 9781617290183 1617290181
OCLC Number: 746834657
Description: xxvi, 354 pages : illustrations ; 24 cm
Contents: Part 1. Classification --
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 --
Part 2. Forecasting numeric values with regression --
8. Predicting numeric values: regression --
9. 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.
Responsibility: Peter Harrington.
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Abstract:

"Machine Learning in Action" blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. The author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features.

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