Introduction to Clustering | Applied Unsupervised Learning with Python

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Applied Unsupervised Learning with Python

By : Benjamin Johnston , Aaron Jones , Christopher Kruger

3 (2)

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Applied Unsupervised Learning with Python

3 (2)

By: Benjamin Johnston , Aaron Jones , Christopher Kruger

Buy this Book

Overview of this book

Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products. By the end of this book, you will have the skills you need to confidently build your own models using Python.

Applied Unsupervised Learning with Python

Preface

Free Chapter

Introduction to Clustering

Introduction

Unsupervised Learning versus Supervised Learning

Clustering

Introduction to k-means Clustering

Activity 1: Implementing k-means Clustering

Summary

Hierarchical Clustering

Introduction

Clustering Refresher

The Organization of Hierarchy

Introduction to Hierarchical Clustering

Linkage

Agglomerative versus Divisive Clustering

k-means versus Hierarchical Clustering

Summary

Neighborhood Approaches and DBSCAN

Introduction

Introduction to DBSCAN

DBSCAN Versus k-means and Hierarchical Clustering

Summary

Dimension Reduction and PCA

Introduction

Overview of Dimensionality Reduction Techniques

PCA

Summary

Autoencoders

Introduction

Fundamentals of Artificial Neural Networks

Autoencoders

Summary

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Introduction

Stochastic Neighbor Embedding (SNE)

t-Distributed SNE

Interpreting t-SNE Plots

Summary

Topic Modeling

Introduction

Cleaning Text Data

Latent Dirichlet Allocation

Non-Negative Matrix Factorization

Summary

Market Basket Analysis

Introduction

Market Basket Analysis

Characteristics of Transaction Data

Apriori Algorithm

Association Rules

Summary

Hotspot Analysis

Introduction

Kernel Density Estimation

Hotspot Analysis

Summary

Appendix

Chapter 1: Introduction to Clustering

Chapter 2: Hierarchical Clustering

Chapter 3: Neighborhood Approaches and DBSCAN

Chapter 4: Dimension Reduction and PCA

Chapter 5: Autoencoders

Chapter 6: t-Distributed Stochastic Neighbor Embedding (t-SNE)

Chapter 7: Topic Modeling

Chapter 8: Market Basket Analysis

Chapter 9: Hotspot Analysis

Applied Unsupervised Learning with Python

By : Benjamin Johnston , Aaron Jones , Christopher Kruger

Applied Unsupervised Learning with Python

By: Benjamin Johnston , Aaron Jones , Christopher Kruger

Overview of this book

Introduction

Confirmation

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