Machine Learning Using Python
Publication details: New Delhi : Wiley India, 2019Description: 343ISBN:- 9788126579907
- 006.31 PRA
Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds | |
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Book | Alliance College of Engineering and Design | CSE & IT | 006.31 PRA (Browse shelf(Opens below)) | 4 | Available | E13174 | |||
Book | Alliance College of Engineering and Design SC & ST Book Bank - Mezzanine Floor | CSE & IT | 006.31 PRA (Browse shelf(Opens below)) | 2 | Available | E13172 | |||
Book | Alliance College of Engineering and Design | CSE & IT | 006.31 PRA (Browse shelf(Opens below)) | 5 | Available | E13175 | |||
Reference Book | Alliance College of Engineering and Design | CSE & IT | 006.31 PRA (Browse shelf(Opens below)) | 1 | Not for loan | E13171 | |||
Book | Alliance College of Engineering and Design | CSE & IT | 006.31 PRA (Browse shelf(Opens below)) | 3 | Available | E13173 | |||
Book | Alliance College of Engineering and Design | CSE & IT | 006.31 PRA (Browse shelf(Opens below)) | 6 | Available | E13176 | |||
Book | Alliance College of Engineering and Design | CSE & IT | 006.31 PRA (Browse shelf(Opens below)) | Checked out | 06/01/2025 | E12270 | |||
Book | Alliance College of Engineering and Design | CSE & IT | 006.31 PRA (Browse shelf(Opens below)) | Available | E11957 | ||||
Book | Alliance College of Engineering and Design | CSE & IT | 006.31 PRA (Browse shelf(Opens below)) | Available | E11958 |
This book is written to provide a strong foundation in machine learning using Python libraries by providing real-life case studies and examples. It covers topics such as foundations of machine learning, introduction to Python, descriptive analytics and predictive analytics. Advanced machine learning concepts such as decision tree learning, random forest, boosting, recommended systems, and text analytics are covered. The book takes a balanced approach between theoretical understanding and practical applications. All the topics include real-world examples and provide step-by-step approach on how to explore, build, evaluate, and optimize machine learning models
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