Machine Learning for Beginners (notice n° 76368)
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| fixed length control field | 04244cam a2200277zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88939136 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250108000938.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250108s2020 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789389845426 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88939136 |
| 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 | Bhasin, Harsh |
| 245 01 - TITLE STATEMENT | |
| Title | Machine Learning for Beginners |
| Remainder of title | Learn to Build Machine Learning Systems Using Python (English Edition) |
| Statement of responsibility, etc. | ['Bhasin, Harsh'] |
| 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 | 2020 |
| 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. | Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key FeaturesUnderstand the types of Machine learning. Get familiar with different Feature extraction methods. Get an overview of how Neural Network Algorithms work.Learn how to implement Decision Trees and Random Forests. The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling.Description This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. What will you learnLearn how to prepare Data for Machine Learning.Learn how to implement learning algorithms from scratch.Use scikit-learn to implement algorithms.Use various Feature Selection and Feature Extraction methods.Learn how to develop a Face recognition system. Who this book is for The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular.Table of Contents 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography About the Author Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development.Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship.Outside work, he is deeply interested in Hindi Poetry, progressive era; Hindustani Classical Music, percussion instruments.His areas of interest include Data Structures, Algorithms Analysis and Design, Theory of Computation , Python, Machine Learning and Deep learning. Your LinkedIn Profile:https://in.linkedin.com/in/harsh-bhasin-69134426 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Bhasin, Harsh |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88939136">https://international.scholarvox.com/netsen/book/88939136</a> |
| Electronic format type | text/html |
| Host name | |
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