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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Spectral clustering

One of the most common problems with K-means and other similar algorithms is the assumption that we only have hyperspherical clusters. In fact, K-means is insensitive to the angle and assigns a label only according to the closest distance between a point and centroids. The resulting geometry is based on hyperspheres where all points share the same condition to be closer to the same centroid. This condition might be acceptable when the dataset is split into blobs that can be easily embedded into a regular geometric structure. However, it fails whenever the sets are not separable using regular shapes. Let's consider, for example, the following bidimensional dataset:

Sinusoidal dataset

As we are going to see in the example, any attempt to separate the upper sinusoid from the lower one using K-means will fail. The reason is obvious: a circle that contains the upper set will also contain part of the (or the whole) lower set. Considering the criterion...