Machine learning using python
By: Pradhan, Manaranjan
.
Contributor(s): Kumar, U. Dinesh.
Publisher: New Delhi Wiley India 2019Description: xx, 343p. | Binding - Paperback | 24*18.2 cm.ISBN: 9788126579907.Subject(s): EXTC EngineeringDDC classification: 006.31 Summary: 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, recommender 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.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Text Books
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Departmental Library - SOET General Stacks | Circulation | 006.31 PRA/KUM (Browse shelf) | Available | E15186 |
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| 005.757 GUP/SAB Practical mongoDB | 006.3 NOR Beginning artificial intelligence with the raspberry pi | 006.31 PAT Pro deep learning with tensorflow | 006.31 PRA/KUM Machine learning using python | 006.76 COL Pro HTML5 with CSS, Javascript, and multimedia | 006.76 JAP/GRO Building web applications with visual studio 2017 | 006.76 RUB Beginning django |
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, recommender 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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