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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Advanced Clustering and Unsupervised Models

In this chapter, we will continue to analyze clustering algorithms, focusing our attention on more complex models that can solve problems where K-means fails. These algorithms are extremely helpful in specific contexts (for example, geographical segmentation) where the structure of the data is highly non-linear and any approximation leads to a substantial drop in performance.

In particular, the algorithms and the topics we are going to analyze are:

  • Fuzzy C-means
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  • DBSCAN, including the Calinski-Harabasz and Davies-Bouldin scores

The first model is Fuzzy C-means, which is an extension of K-means to a soft-labeling scenario. Just like Generative Gaussian Mixtures, the algorithm helps the data scientist to understand the pseudo-probability (a measure similar to an actual probability) of a data point belonging to all defined clusters.