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)
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Index

Generative Gaussian Mixture

The first model we're going to discuss is called Generative Gaussian Mixture, and it aims to model the data generating process pdata using a sum of weighted Gaussian distributions. Since the model is generative, its structure allows us not only to cluster the existing dataset into well-defined regions (represented as Gaussians), but also to output the probability of any new data point to belong to each of the classes. This model is very flexible, and can be applied to solve all those problems where it's necessary to perform a clustering and a classification at the same time, obtaining the assignment probability vector that determines the likelihood of a data point to be generated by a specific Gaussian distribution.

Generative Gaussian Mixture theory

Generative Gaussian Mixture is an inductive algorithm for semi-supervised classification and clustering that's aimed at modeling the conditional probability given both a...