Hands-on unsupervised learning using Python : how to build applied machine learning solutions from unlabeled data / Ankur A. Patel.

Format:

Book

Author/Creator:

Patel, Ankur A., author.

Language:

English

Subjects (All):

Python (Computer program language).

Machine learning.

Artificial intelligence.

Physical Description:

1 online resource (340 pages) : illustrations

Edition:

First edition.

Other Title:

Sub-title on cover: How to build applied machine learning solutions from unlabeled data

Place of Publication:

Beijing : O'Reilly, [2019]

System Details:

text file

Summary:

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, 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 shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists 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 machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks

Contents:

Part 1. Fundamentals of unsupervised learning. Unsupervised learning in the machine learning ecosystem

End-to-end machine learning project

Part 2. Unsupervised learning using Scikit-learn. Dimensionality reduction

Anomaly detection

Clustering

Group segmentation

Part 3. Unsupervised learning using TensorFlow and Keras. Autoencoders

Hands-on autoencoder

Semisupervised learning

Part 4. Deep unsupervised learning using TensorFlow and Keras. Recommender systems using restricted Boltzmann machines

Feature detection using deep belief networks

Generative adversarial networks

Time series clustering

Conclusion.

Notes:

Description based on print version record.

Includes bibliographical references.

ISBN:

9781492035596

1492035599

9781492035633

1492035637

9781492035619

1492035610

OCLC:

1089256249