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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

Christopher Kruger

Aaron Jones

View More author details

Table of Contents (12) Chapters

Applied Unsupervised Learning with Python

Preface

1. Introduction to Clustering

2. Hierarchical Clustering FREE CHAPTER

3. Neighborhood Approaches and DBSCAN

4. Dimension Reduction and PCA

5. Autoencoders

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

7. Topic Modeling

8. Market Basket Analysis

9. Hotspot Analysis

Appendix

Introduction

Let's consider an imaginary scenario: a new disease has begun spreading through numerous communities in the country that you live in and the government is trying to figure out how to confront this health emergency. Critical to any plan to confront this health emergency is epidemiological knowledge, including where the patients are located and how the disease is moving. The ability to locate and quantify problem areas (which are classically referred to as hotspots) can help health professionals, policy makers, and emergency response teams craft the most effective and efficient strategies for combating the disease. This scenario highlights one of the many applications of hotspot modeling.

Hotspot modeling is an approach that is used to identify how a population is distributed across a geographical area; for example, how the population of individuals infected with the previously mentioned disease is spread across the country. The creation of this distribution relies on the availability...

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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.

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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.

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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.

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