Machine learning in action/ By Peter Harrington.

By:

Harrington, Peter

Material type: Text

TextPublication details: New Delhi: Dreamtech press, c2012.Description: i-xxvi, 354 pISBN:

9789350044131

Subject(s):

Machine learning -- Handbooks, manuals, etc. Engineering & Applied Sciences

DDC classification:

006.3 HAR-M

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.

Summary: 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. 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 this 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. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author 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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Holdings
Item type	Current library	Home library	Collection	Call number	Status	Date due	Barcode
Book	Dept. of Computational Biology and Bioinformatics Processing Center	Dept. of Computational Biology and Bioinformatics		006.3 HAR-M (Browse shelf(Opens below))	Available		DCB3970
Book	Dept. of Futures Studies Processing Center	Dept. of Futures Studies	Wind Forecasting	006.32 HAR (Browse shelf(Opens below))	Available		DFSWF41

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.

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. 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 this 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. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author 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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