What this book covers
Chapter 1, Introduction to Machine Learning, covers various concepts about machine learning. This chapter makes the reader aware of the various topics we shall be covering in the book.
Chapter 2, Classification, covers the following topics and algorithms: discriminant function analysis, multinomial logistic regression, Tobit regression, and Poisson regression.
Chapter 3, Clustering, covers the following topics and algorithms: hierarchical clustering, binary clustering, and k-means clustering.
Chapter 4, Model Selection and Regularization, covers the following topics and algorithms: shrinkage methods, dimension reduction methods, and principal component analysis.
Chapter 5, Nonlinearity, covers the following topics and algorithms: generalized additive models, smoothing splines, local regression.
Chapter 6, Supervised Learning, covers the following topics and algorithms: decision tree learning, Naive Bayes, random forest, support vector machine, and stochastic gradient descent.
Chapter 7, Unsupervised Learning, covers the following topics and algorithms: self-organizing map, and vector quantization.
Chapter 8, Reinforcement Learning, covers the following topics and algorithms: Markov chains, and Monte Carlo simulations.
Chapter 9, Structured Prediction, covers the following topic and algorithms: hidden Markov models.
Chapter 10, Neural Networks, covers the following topic and algorithms: neural networks.
Chapter 11, Deep Learning, covers the following topic and algorithms: recurrent neural networks.
Chapter 12, Case Study - Exploring World Bank Data, covers World Bank data analysis.
Chapter 13, Case Study - Pricing Reinsurance Contracts, covers pricing reinsurance contracts.
Chapter 14, Case Study - Forecast of Electricity Consumption, covers forecasting electricity consumption.
