Machine learning in action
By: Harrington, Peter.
Publisher: New Delhi Dreamtech Press 2012Description: xxv,354p. | Binding - Paperback |.ISBN: 978-93-5004-413-1.Subject(s): Computer Engineering
DDC classification: 6.31
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School of Engineering & Technology Reference Section | Reference | 006.31 HAR (Browse shelf) | Not For Loan | E12964 | ||
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Text Books
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School of Engineering & Technology General Stacks | Circulation | 006.31 HAR (Browse shelf) | Checked out to Irfan Rafiq Jamkhandikar (COF011) | 25/11/2021 | E12965 | |
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School of Engineering & Technology General Stacks | Circulation | 006.31 HAR (Browse shelf) | Checked out to Irfan Badiyoddin Shaikh (CEF030) | 04/04/2024 | E12966 | |
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School of Engineering & Technology General Stacks | Circulation | 006.31 HAR (Browse shelf) | Available | E12967 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 HAR (Browse shelf) | Available | E12968 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 HAR (Browse shelf) | Available | E12969 |
“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 will 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 will explore key topics like classification, numeric prediction, and clustering. Along the way, you will 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. This book is written for hobbyists and developers. A background in Java is helpful-no prior experience with Android is assumed. Special Features Learning Elements in this book: · An easy to follow introduction to machine learning · Automatically classifying data for more precise analysis · Forecasting values · Building recommendation engines Some programming background is helpful, but no prior knowledge of Python or machine learning techniques is required. Table of Content Part 1- Classification · 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 101 · Improving classification with the AdaBoost meta-algorithm Part 2- Forecasting Numeric Values With Regression · Predicting numeric values: regression 153 · Tree-based regression Part 3- Unsupervised Learning · Grouping unlabeled items using k-means clustering · Association analysis with the Apriori algorithm · Efficiently finding frequent itemsets with FP-growth Part 4- Additional Tools · Using principal component analysis to simplify data · Simplifying data with the singular value decomposition · Big data and MapReduce
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