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Condition: New
ISBN-13: 9798341607989
Format: Paperback
A practical machine learning book that teaches by building, using Scikit-Learn for classical methods and PyTorch for deep learning.
It moves from the shape of a machine learning project and the handling of data through regression and classification, support vector machines, decision trees and ensembles, dimensionality reduction and unsupervised methods, then into neural networks, training deep models, and architectures for vision and sequence data, with code worked through rather than described.
For developers and data scientists learning machine learning in practice, and for students who want an applied companion to a theory course.
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