Table of Contents for
Hands-On Unsupervised Learning Using Python

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Hands-On Unsupervised Learning Using Python by Ankur A. Patel Published by O'Reilly Media, Inc., 2019
  1. Cover
  2. nav
  3. Hands-on Unsupervised Learning Using Python
  4. Hands-On Unsupervised Learning Using Python
  5. Preface
  6. I. Fundamentals of Unsupervised Learning
  7. 1. Unsupervised Learning in the Machine Learning Ecosystem
  8. 2. End-to-End Machine Learning Project
  9. II. Unsupervised Learning Using Scikit-Learn
  10. 3. Dimensionality Reduction
  11. 4. Anomaly Detection
  12. 5. Clustering
  13. 6. Group Segmentation
  14. III. Unsupervised Learning using TensorFlow and Keras
  15. 7. Autoencoders
  16. 8. Hands-On Autoencoder
  17. 9. Semi-Supervised Learning
  18. IV. Deep Unsupervised Learning using TensorFlow and Keras
  19. 10. Recommender Systems Using Restricted Boltzmann Machines
  20. 11. Feature Detection Using Deep Belief Networks
  21. 12. Generative Adversarial Networks
  22. 13. Time Series Clustering
  23. 14. Conclusion
  24. Index
  25. About the Author

Part II. Unsupervised Learning Using Scikit-Learn

In the next few chapters, we will introduce two major unsupervised learning concepts - dimensionality reduction and clustering - and use these to perform anomaly detection and group segmentation.

Both anomaly detection and group segmentation have significant real-world applications across many different industries.

Anomaly detection is used to efficiently discover rare events such as fraud, cybersecurity breaches, terrorism, human, arms, and drug trafficking, money laundering, abnormal trading activity, disease outbreaks, and maintenance failures in mission-critical equipment.

Group segmentation is used to understand user behavior in areas such as marketing, online shopping, music listening, video watching, online dating, and social media activity, among others.