Hands-On Unsupervised Learning Using Python

Hands-On Unsupervised Learning Using Python

Computers & Technology
ISBN: 1492035645Format: EPUBEdition: 1 editionDate: March 18, 2019Pages: 362 pagesLanguage: English

Download Hands-On Unsupervised Learning Using Python


Download .EPUB eBook

Book Description

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.

Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow using Keras. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.

  • Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning
  • Set up and manage a machine learning project end-to-end - everything from data acquisition to building a model and implementing a solution in production
  • Use dimensionality reduction algorithms to uncover the most relevant information in data and build an anomaly detection system to catch credit card fraud
  • Apply clustering algorithms to segment users - such as loan borrowers - into distinct and homogeneous groups
  • Use autoencoders to perform automatic feature engineering and selection
  • Combine supervised and unsupervised learning algorithms to develop semi-supervised solutions
  • Build movie recommender systems using restricted Boltzmann machines
  • Generate synthetic images using deep belief networks and generative adversarial networks
  • Perform clustering on time series data such as electrocardiograms
  • Explore the successes of unsupervised learning to date and its promising future

Content

Part I. Fundamentals of Unsupervised Learning
Chapter 1. Unsupervised Learning in the Machine Learning Ecosystem
Chapter 2. End-to-End Machine Learning Project

Part II. Unsupervised Learning Using Scikit-Learn
Chapter 3. Dimensionality Reduction
Chapter 4. Anomaly Detection
Chapter 5. Clustering
Chapter 6. Group Segmentation

Part III. Unsupervised Learning Using TensorFlow and Keras
Chapter 7. Autoencoders
Chapter 8. Hands-On Autoencoder
Chapter 9. Semisupervised Learning

Part IV. Deep Unsupervised Learning Using TensorFlow and Keras
Chapter 10. Recommender Systems Using Restricted Boltzmann Machines
Chapter 11. Feature Detection Using Deep Belief Networks
Chapter 12. Generative Adversarial Networks
Chapter 13. Time Series Clustering
Chapter 14. Conclusion

Book cover


Hands-On Unsupervised Learning Using Python
В закладки

Dear users and students. The Book Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data on our website it is presented for demonstration only. We do not store the files, If you like the book, please remove it and to buy a printed version of the book.

If You feel that this book is belong to you and you want to unpublish it, Please Contact us.

This site comply with DMCA digital copyright. We do not store files not owned by us, or without the permission of the owner. We also do not have links that lead to sites DMCA copyright infringement.



Comments (0)
ADD COMMENTS
Прокомментировать
reload, if the code cannot be seen
Python: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents using Python
Hands-On Unsupervised Learning with Python: Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
Python Machine Learning Cookbook
Applied Unsupervised Learning with R
Hands-On Machine Learning with IBM Watson
TensorFlow 2.0 Quick Start Guide: Get up to speed with the newly introduced features of TensorFlow 2.0
Keras to Kubernetes: The Journey of a Machine Learning Model to Production
Natural Computing for Unsupervised Learning