What this book covers

Chapter 1, Machine Learning Model Fundamentals, explains the most important theoretical concepts regarding machine learning models, including bias, variance, overfitting, underfitting, data normalization, and cost functions. It can be skipped by those readers with a strong knowledge of these concepts.

Chapter 2, Introduction to Semi-Supervised Learning, introduces the reader to the main elements of semi-supervised learning, focusing on inductive and transductive learning algorithms.

Chapter 3, Graph-Based Semi-Supervised Learning, continues the exploration of semisupervised learning algorithms belonging to the families of graph-based and manifold learning models. Label propagation and non-linear dimensionality reduction ...

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