Machine Learing : Theory and Practice / By Jugal Kalita
By: Kalita ,Jugal [author].
Publisher: New York: CRC Press, 2023Description: xv,282p.ISBN: 9780367433543.Subject(s): Machine Learning -- Ensemble learning -- Explanation-based learning | Artificial Intelligence | Machine theoryDDC classification: 006.31 Summary: Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples| Item type | Current location | Call number | Status | Date due | Barcode |
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NASSDOC Library | 006.31 KAL-M (Browse shelf) | Available | 53974 |
Include Bibliography and Indexes.
Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples
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