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Covers popular machine learning methods including regression, trees, neural networks, reinforcement learning, and clustering for a broad foundation
Provides lucid mathematical explanations and figures to ensure firm conceptual understanding
Includes worked programming examples in R to reinforce both theory and practice
Designed for advanced undergraduates and beginning graduate students with a focus on math and programming
concise coverage of key topics within a semester or quarter, enabling focused learning
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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 N