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Applied Unsupervised Learning with Python

You're reading from Applied Unsupervised Learning with Python Discover hidden patterns and relationships in unstructured data with Python

Product type Paperback

Published in May 2019

Publisher

ISBN-13 9781789952292

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (3):

Benjamin Johnston

Aaron Jones

Christopher Kruger

View More author details

Table of Contents (20) Chapters

Preface

About the Book

1. Chapter 1 FREE CHAPTER

2. Introduction to Clustering

3. Chapter 2

4. Hierarchical Clustering

5. Chapter 3

6. Neighborhood Approaches and DBSCAN

7. Chapter 4

8. Dimension Reduction and PCA

9. Chapter 5

10. Autoencoders

11. Chapter 6

12. t-Distributed Stochastic Neighbor Embedding (t-SNE)

13. Chapter 7

14. Topic Modeling

15. Chapter 8

16. Market Basket Analysis

17. Chapter 9

18. Hotspot Analysis

Appendix

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Learning Objectives

By the end of this chapter, you will be able to:

Describe and understand the motivation behind t-SNE
Describe the derivation of SNE and t-SNE
Implement t-SNE models in scikit-learn
Explain the limitations of t-SNE

In this chapter, we will discuss Stochastic Neighbor Embedding (SNE) and t-Distributed Stochastic Neighbor Embedding (t-SNE) as a means of visualizing high-dimensional datasets.

The rest of the chapter is locked

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Authors (3)

Benjamin Johnston

Benjamin Johnston

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.

See other products by Benjamin Johnston

Aaron Jones

Aaron Jones

Aaron Jones is a full-time senior data scientist and consultant. He has built models and data products while working in retail, media, and environmental science. Aaron is based in Seattle, Washington and has a particular interest in clustering algorithms, natural language processing, and Bayesian statistics.

See other products by Aaron Jones

Christopher Kruger

Christopher Kruger

Christopher Kruger is a practicing data scientist and AI researcher. He has managed applied machine learning projects across multiple industries while mentoring junior team members on best practices. His primary focus is on pushing both business practicality as well as academic rigor in every project. Chris is currently developing research in the computer vision space.

See other products by Christopher Kruger

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