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It offers a theoretical account of the fundamentals of the concept, including the mathematical derivations which transform these principles into practical working algorithms. The book starts off with a chapter purely based on Foundations, which defines learning, introduces the concept of a learning model, bias, the VC Vapnik- Chervonenkis dimension to get the reader to adapt to the new concept.

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The book then moves onto the theory and algorithmic part of the models, including various types of learning problems, the various methods to solve them which include Linear Predictors, Stochastic Gradient Descent, Decision Trees, Nearest Neighbours, and Neural Networks etc. It finally transcends into more advanced algorithms of Online Learning- which is one of the most sought- after techniques used in business like e- commerce, dives deeper into efficient feature selections and generations, fundamental theorem of learning theory, and ends with the emerging theoretical concept of PAC-Bayes approach.

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