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 some fundamental clustering algorithms. We started with KNN, which is an instance-based method that restructures the dataset to find the most similar samples to a given query point. We discussed three approaches: a naive one, which is also the most expensive in terms of computational complexity, and two strategies based respectively on the construction of a k-d tree and a ball tree. These two data structures can dramatically improve performance even when the number of samples is very large.

The next topic was a classic algorithm: K-means, which is a symmetric partitioning strategy comparable to a Gaussian mixture with variances close to zero that can solve many real-life problems. We discussed both a vanilla algorithm, which couldn't find a valid sub-optimal solution, and an optimized initialization method, called K-means++, which was able to speed up the convergence toward solutions quite close to the global minimum.

We also presented...