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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Biclustering is a family of methods that operate on matrices whose rows and columns represent different features connected with a precise rationale. For example, the rows can represent customers, and the columns products. Each element can indicate a rating or, if zero, the fact that a specific product, pj, has not been bought/rated by the customer, ci. As the behavior of the customers can generally be segmented into specific sets, we can assume that A has an underlying checkerboard structure, where the compact regions, called biclusters, represent sub-matrices with peculiar properties.

The nature of such properties depends on the specific context, but the structures share the common feature of being strongly separated from the remaining regions. In our example, the biclusters can be mixed segments containing sets of customers and products that agree on the rating (this concept will be clearer in the practical example), but more generally, the rearrangement of rows...