Packt+ | Advance your knowledge in tech

Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

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 FREE CHAPTER

2. Hierarchical Clustering

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

Chapter 7. Topic Modeling

Note

Learning Objectives

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

Perform basic cleaning techniques for textual data
Evaluate latent Dirichlet allocation models
Execute non-negative matrix factorization models
Interpret the results of topic models
Identify the best topic model for the given scenario

Note

In this chapter, we will see how topic modeling provides insights into the underlying structure of documents.

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $19.99/month. Cancel anytime

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

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

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

Other recommended products

Related to this chapter

The Unsupervised Learning Workshop

The Unsupervised Learning Workshop

With the help of engaging practical activities, The Unsupervised Learning Workshop teaches you how to apply unsupervised machine learning algorithms on enormous, cluttered datasets. You'll learn the best techniques and approaches and work on real-time datasets with this hands-on guide for beginners.

Jul 2020 18h 20m

The Unsupervised Learning Workshop

The Unsupervised Learning Workshop

With the help of engaging practical activities, The Unsupervised Learning Workshop teaches you how to apply unsupervised machine learning algorithms on enormous, cluttered datasets. You'll learn the best techniques and approaches and work on real-time datasets with this hands-on guide for beginners.

Jul 2020 18h 20m

The Unsupervised Learning Workshop

The Unsupervised Learning Workshop

With the help of engaging practical activities, The Unsupervised Learning Workshop teaches you how to apply unsupervised machine learning algorithms on enormous, cluttered datasets. You'll learn the best techniques and approaches and work on real-time datasets with this hands-on guide for beginners.

Jul 2020 18h 20m

The Unsupervised Learning Workshop

The Unsupervised Learning Workshop

With the help of engaging practical activities, The Unsupervised Learning Workshop teaches you how to apply unsupervised machine learning algorithms on enormous, cluttered datasets. You'll learn the best techniques and approaches and work on real-time datasets with this hands-on guide for beginners.

Jul 2020 18h 20m

The Unsupervised Learning Workshop

The Unsupervised Learning Workshop

With the help of engaging practical activities, The Unsupervised Learning Workshop teaches you how to apply unsupervised machine learning algorithms on enormous, cluttered datasets. You'll learn the best techniques and approaches and work on real-time datasets with this hands-on guide for beginners.

Jul 2020 18h 20m

The Unsupervised Learning Workshop

The Unsupervised Learning Workshop

With the help of engaging practical activities, The Unsupervised Learning Workshop teaches you how to apply unsupervised machine learning algorithms on enormous, cluttered datasets. You'll learn the best techniques and approaches and work on real-time datasets with this hands-on guide for beginners.

Jul 2020 18h 20m

Applied Unsupervised Learning with R

Applied Unsupervised Learning with R

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions.

Mar 2019 10h 40m

Applied Unsupervised Learning with R

Applied Unsupervised Learning with R

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions.

Mar 2019 10h 40m

Applied Unsupervised Learning with R

Applied Unsupervised Learning with R

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions.

Mar 2019 10h 40m

Applied Unsupervised Learning with R

Applied Unsupervised Learning with R

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions.

Mar 2019 10h 40m

Applied Unsupervised Learning with R

Applied Unsupervised Learning with R

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions.

Mar 2019 10h 40m

Applied Unsupervised Learning with R

Applied Unsupervised Learning with R

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions.

Mar 2019 10h 40m

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m