Applied unsupervised learning with Python : discover hidden patterns and relationships in unstructured data with Python / Benjamin Johnston, Aaron Jones, Christopher Kruger.

Format:

Book

Author/Creator:

Johnston, Benjamin, author.

Jones, Aaron, author.

Kruger, Christopher, author.

Language:

English

Subjects (All):

Python (Computer program language).

Physical Description:

1 online resource (483 pages)

Edition:

1st edition

Place of Publication:

Birmingham, UK : Packt Publishing, 2019.

System Details:

Mode of access: World Wide Web.

text file

Summary:

Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Key Features Learn how to select the most suitable Python library to solve your problem Compare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them Delve into the applications of neural networks using real-world datasets Book Description 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. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this book, you will have the skills you need to confidently build your own models using Python. What you will learn Understand the basics and importance of clustering Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages Explore dimensionality reduction and its applications Use scikit-learn (sklearn) to implement and analyze principal component analysis (PCA) on the Iris dataset Employ Keras to build autoencoder models for the CIFAR-10 dataset Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data Who this book is for This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial. Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at ...

Contents:

Applied unsupervised learning with Python: discover hidden patterns and relationships in unstructured data with Python

Table of Contents

Preface

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

Appendix

Index.

Notes:

Includes index.

Includes bibliographical references.

Description based on print version record.

ISBN:

1-78995-837-7

OCLC:

1123174441