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)
26
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27
Index

Clustering and Unsupervised Models for Marketing

This chapter is dedicated to two methods that can be extremely helpful in marketing applications. Unsupervised learning has many interesting applications in contexts where it's necessary to structure the knowledge a business has about customers, in order to optimize promotional campaigns, recommendations, or marketing strategies. This chapter shows how it's possible to exploit a particular kind of clustering to find similarities among sets of customers and products, and how to extract logic rules that describe and synthesize the behavior of customers selecting products from a catalog. Using these rules, marketeers can understand how to optimize their promotions, how to rearrange the position of their products, and what other items could be successfully suggested when a purchase is made.

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

  • Biclustering based on a spectral biclustering...