Applied machine learning M. Gopal
Material type: TextPublication details: United States of America : McGraw-Hill Education , 2019.Description: xix, 630 pages : figures, tables ; 27 cmContent type:- text
- unmediated
- volume
- 9781260456844
- 006.31 G646a
Item type | Current library | Shelving location | Call number | Copy number | Status | Date due | Barcode | |
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Books | Main Library-Nabua | Engineering Section | ENG 006.31 G646a 2019 (Browse shelf(Opens below)) | 1-2 | Available | 023618 | ||
Books | Main Library-Nabua | Engineering Section | ENG 006.31 G646a 2019 (Browse shelf(Opens below)) | 2-2 | Available | 023619 |
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ENG 006.22 V59e 2019 Embedded system & microcontroller | ENG 006.22 V59e 2019 Embedded system & microcontroller | ENG 006.31 G646a 2019 Applied machine learning | ENG 006.31 G646a 2019 Applied machine learning | ENG 006.42 L612a 2017 Architecture-aware optimization strategies in real-time image processing Chao Li | ENG 006.42 L612a 2017 Architecture-aware optimization strategies in real-time image processing Chao Li | ENG 174.962 G747e 2014 Engineering ethics includes human values/ |
Includes index,
Includes bibliographical references pages (613-622).
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical underƠpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employed. Coverage includes: •Supervised learning •Statistical learning •Learning with support vector machines (SVM) •Learning with neural networks (NN) •Fuzzy inference systems •Data clustering •Data transformations •Decision tree learning •Business intelligence •Data mining •And much more
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