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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In this chapter, we presented a soft-clustering method called Fuzzy C-means, which resembles the structure of standard K-means but allows managing membership degrees (analogous to probabilities) that encode the similarity of a sample with all cluster centroids. This kind of approach allows the processing of membership vectors in a more complex pipeline, where the output of a clustering process, for example, is fed into a classifier.

One of the most important limitations of K-means and similar algorithms is the symmetric structure of the clusters. This problem can be solved with methods such as spectral clustering, which is a very powerful approach based on the dataset graph and is quite similar to non-linear dimensionality reduction methods. We analyzed an algorithm proposed by Shi and Malik, showing how it can easily separate a non-convex dataset.

We also discussed a completely geometry-agnostic algorithm, DBSCAN, which is helpful when it's necessary to discover...