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008			250220b |||||||| |||| 00| 0 eng d
020			_a9789354601590
082			_a006.31 _bGOP
100			_aGopal, M. _eEx. Professor, Indian Institute of Technology Delhi
245			_aApplied Machine Learning
250			_a2nd ed
260			_aDelhi _bMcGraw Hill Education _c2024
300			_axx, 526p _c24 x 18 cm
500			_aOverview Now in its second edition, Applied Machine Learning continues to explore the theoretical underpinnings of learning and equips the readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. This book shows in a step-by-step manner how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning methods that have been profitably employed. It also provides a platform for hands-on experience through case studies and exercise experiments. Key Features • Simple and lucid explanation of all basic concepts and techniques of machine learning, well supported with case studies and exercise experiments • New sections on widely used techniques, such as log loss (cross entropy) metric for classification, imbalanced data problems and solutions, Ridge and LASSO regression, multiclass logistic regression, softmax function, and many more • New chapter on ‘Understanding Machine Learning by Application’ provides a platform for gentle start on hands-on experience through case studies on real-life problems in diverse and important application areas • Datasets in Excel files for Case studies and Exercise Experiments available online (refer to the complete list on the back inner cover)
520			_a1. Introduction 2. Supervised Learning: Rationale and Basics 3. Statistical Learning 4. Learning with Support Vector Machines (SVMs) 5. Learning with Neural Networks (NNs) 6. Decision Tree Learning and Tree-Based Ensembles 7. Data Clustering 8. Data Transformations 9. Understanding Machine Learning by Application Appendix A: The Basic Genetic Algorithm (GA) for Search Optimization Appendix B: Basic Linear Algebra in Machine Learning Techniques Problems References Index
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