000			01220nam a22001817a 4500
999			_c38795 _d38795
020			_a9780367433543
082			_a006.31 _bKAL-M
100			_aKalita ,Jugal _eauthor
245			_aMachine Learing : _bTheory and Practice / _cBy Jugal Kalita
260			_aNew York: _bCRC Press, _c2023.
300			_axv,282p.
504			_aInclude Bibliography and Indexes.
520			_aMachine 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
650			_aMachine Learning _vEnsemble learning _xExplanation-based learning
650			_aArtificial Intelligence
650			_aMachine theory
942			_2ddc _cBK