Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
Authors: Ankur A. Patel
Publisher finelybook 出版社: O’Reilly Media; (April 16, 2019)
Language 语言: English
Print Length 页数: 359 pages
ISBN-10: 1492035645
ISBN-13: 9781492035640
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
Preface
I.Fundamentals of Unsupervised Learning
1.Unsupervised Learning in the Machine Learning Ecosystem
2.End-to-End Machine Learning Project
II.Unsupervised Learning Using Scikit-Learn
3.Dimensionality Reduction
4.Anomaly Detection
5.Clustering
6.Group Segnentation
III.Unsupervised Learning Using TensorFlow and Keras
7.Autoencoders
8.Hands-On Autoencoder
9.Semi supervised Learning
IV.Deep Unsupervised Learning Using TensorFlow and Keras
10.Recommender Systems Using Restricted Boltzmann Machines
11.Feature Detection Using Deep Belief Networks
12.Generative Adversarial Networks
13.Time Series Clustering
14.Conclusion
Index
