Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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27
Index

Generalized Linear Models and Regression

In this chapter, we're going to introduce the concept of Generalized Linear Models (GLMs) and regression, which remain essential pillars of topics such as econometrics and epidemiology. The goal is to explain the fundamental elements and expand them, showing both the advantages and limitations, while also focusing attention on practical applications that can be effectively solved using different kind of regression techniques.

In particular, we're going to discuss the following:

  • GLMs
  • Linear regression based on ordinary and weighted least squares
  • Other regression techniques and when to use them, including:
    • Ridge regression and its implementation
    • Polynomial regression with coded examples
    • Isotonic regression
    • Risk modeling with lasso and logistic regression

The first concept we're going to discuss is at the center of all the other algorithms analyzed in this chapter, and which is based on the description...