Applied machine learning
Material type:
TextPublication details: Switzerland Springer 2019 Description: xxi, 494 pISBN: 9783030181161Subject(s): Machine learning | Mechanical engineering | Artificial intelligence | Computer scienceDDC classification: 006.31 Summary: About the Book
Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code.
A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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Book
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Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 006.31 FOR (Browse shelf(Opens below)) | 1 | Available | 002762 |
About the Book
Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code.
A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).
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