Machine Learning for Absolute Beginners by Oliver Theobald

Machine Learning for Absolute Beginners by Oliver Theobald

Most machine learning books assume linear algebra on page one. Theobald assumes nothing, and spends the first chapters simply explaining what the field is trying to do before showing you a single formula.

It covers the vocabulary — supervised and unsupervised learning, training and test data, overfitting — and then walks through the main algorithms one at a time: regression, clustering, decision trees, random forests, support vector machines and neural networks, each with a worked example and diagrams rather than proofs.

The third edition of one of the best-selling introductions in the field, written for people starting from zero.

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