Book Image

Hands-On Unsupervised Learning with Python

By : Giuseppe Bonaccorso

Book Image

Hands-On Unsupervised Learning with Python

By: Giuseppe Bonaccorso

Overview of this book

Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Getting Started with Unsupervised Learning

Getting Started with Unsupervised Learning

Technical requirements

Why do we need machine learning?

Types of machine learning algorithm

Why Python for data science and machine learning?

Further reading

Clustering Fundamentals

Clustering Fundamentals

Technical requirements

Introduction to clustering

Analysis of the Breast Cancer Wisconsin dataset

Evaluation metrics

K-Nearest Neighbors

Vector Quantization

Further reading

Advanced Clustering

Advanced Clustering

Technical requirements

Spectral clustering

Online clustering

Further reading

Hierarchical Clustering in Action

Hierarchical Clustering in Action

Technical requirements

Cluster hierarchies

Agglomerative clustering

Analyzing a dendrogram

Cophenetic correlation as a performance metric

Agglomerative clustering on the Water Treatment Plant dataset

Connectivity constraints

Further reading

Soft Clustering and Gaussian Mixture Models

Soft Clustering and Gaussian Mixture Models

Technical requirements

Soft clustering

Gaussian mixture

Further reading

Anomaly Detection

Anomaly Detection

Technical requirements

Probability density functions

Kernel density estimation (KDE)

Anomaly detection

One-class support vector machines

Anomaly detection with Isolation Forests

Further reading

Dimensionality Reduction and Component Analysis

Dimensionality Reduction and Component Analysis

Technical requirements

Principal Component Analysis (PCA)

Independent Component Analysis

Topic modeling with Latent Dirichlet Allocation

Further reading

Unsupervised Neural Network Models

Unsupervised Neural Network Models

Technical requirements

Hebbian-based principal component analysis

Unsupervised deep belief networks

Further reading

Generative Adversarial Networks and SOMs

Generative Adversarial Networks and SOMs

Technical requirements

Generative adversarial networks

Self-organizing maps

Further reading

Assessments

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Principal Component Analysis (PCA)

One of the most common ways to reduce the dimensionality of a dataset is based on the analysis of the sample covariance matrix. In general, we know that the information content of a random variable is proportional to its variance. For example, given a multivariate Gaussian, the entropy, which is the mathematical expression that we employ to measure the information, is as follows:

In the previous formula, Σ is the covariance matrix. If we assume (without loss of generality) that Σ is diagonal, it's easy to understand that the entropy is larger (proportionally) than the variance of each single component, σ_i². This is not surprising, because a random variable with a low variance is concentrated around the mean, and the probability of surprises is low. On the other hand, when σ² becomes larger and larger, the potential...