Simplified Machine Learning (notice n° 79741)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03652cam a2200277zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88958013 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250108004658.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250108s2024 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789355516145 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88958013 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sharma, Dr. Pooja |
| 245 01 - TITLE STATEMENT | |
| Title | Simplified Machine Learning |
| Remainder of title | The essential building blocks for Machine Learning expertise (English Edition) |
| Statement of responsibility, etc. | ['Sharma, Dr. Pooja'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | BPB Publications |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Explore the world of Artificial Intelligence with a deep understanding of Machine Learning concepts and algorithmsKey Features? A detailed study of mathematical concepts, Machine Learning concepts, and techniques.? Discusses methods for evaluating model performances and interpreting results.? Explores all types of Machine Learning (supervised, unsupervised, reinforcement, association rule mining, artificial neural network) in detail.? Comprises numerous review questions and programming exercises at the end of every chapter.Description"Simplified Machine Learning" is a comprehensive guide that navigates readers through the intricate landscape of Machine Learning, offering a balanced blend of theory, algorithms, and practical applications.The first section introduces foundational concepts such as supervised and unsupervised learning, regression, classification, clustering, and feature engineering, providing a solid base in Machine Learning theory. The second section explores algorithms like decision trees, support vector machines, and neural networks, explaining their functions, strengths, and limitations, with a special focus on deep learning, reinforcement learning, and ensemble methods. The book also covers essential topics like model evaluation, hyperparameter tuning, and model interpretability. The final section transitions from theory to practice, equipping readers with hands-on experience in deploying models, building scalable systems, and understanding ethical considerations.By the end, readers will be able to leverage Machine Learning effectively in their respective fields, armed with practical skills and a strategic approach to problem-solving.What you will learn? Solid foundation in Machine Learning principles, algorithms, and methodologies.? Implementation of Machine Learning models using popular libraries like NumPy, Pandas, PyTorch, or scikit-learn.? Knowledge about selecting appropriate models, evaluating their performance, and tuning hyperparameters.? Techniques to pre-process and engineer features for Machine Learning models.? To frame real-world problems as Machine Learning tasks and apply appropriate techniques to solve them.Who this book is forThis book is designed for a diverse audience interested in Machine Learning, a core branch of Artificial Intelligence. Its intellectual coverage will benefit students, programmers, researchers, educators, AI enthusiasts, software engineers, and data scientists.Table of Contents1. Introduction to Machine Learning2. Data Pre-processing3. Supervised Learning: Regression4. Supervised Learning: Classification5. Unsupervised Learning: Clustering6. Dimensionality Reduction and Feature Selection7. Association Rule Mining8. Artificial Neural Network9. Reinforcement Learning10. ProjectAppendixBibliography |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sharma, Dr. Pooja |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88958013">https://international.scholarvox.com/netsen/book/88958013</a> |
| Electronic format type | text/html |
| Host name | |
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