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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Clustering and Unsupervised Models

In this chapter, we are going to introduce some fundamental clustering algorithms and discuss their strengths and weaknesses. The field of unsupervised learning, as well as any other machine learning approach, must always be based on the concept of Occam's razor. Simplicity must always be preferred, so long as the performance of the model meets your requirements.

However, in this case, the ground truth can be unknown. When a clustering algorithm is adopted as an exploratory tool, we can only assume that the dataset represents a precise data-generating process. If this assumption is correct, the best strategy is to determine the number of clusters to maximize the internal cohesion (denseness) and the external separation. This means that we expect to find blobs (or isles) whose samples share some common and partially unique features.

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

  • K-Nearest Neighbors...