Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data - Softcover

Patel, Ankur A.

Softcover

ISBN 10: 1492035645 ISBN 13: 9781492035640

Publisher: O'Reilly Media, 2019

Synopsis

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

About this title

About the Author

Ankur A. Patel is the Vice President of Data Science at 7Park Data, a Vista Equity Partners portfolio company. At 7Park Data, Ankur and his data science team use alternative data to build data products for hedge funds and corporations and develop machine learning as a service (MLaaS) for enterprise clients. MLaaS includes natural language processing (NLP), anomaly detection, clustering, and time series prediction. Prior to 7Park Data, Ankur led data science efforts in New York City for Israeli artificial intelligence firm ThetaRay, one of the world's pioneers in applied unsupervised learning.

Ankur began his career as an analyst at J.P. Morgan, and then became the lead emerging markets sovereign credit trader for Bridgewater Associates, the world's largest global macro hedge fund, and later founded and managed R-Squared Macro, a machine learning-based hedge fund, for five years. A graduate of the Woodrow Wilson School at Princeton University, Ankur is the recipient of the Lieutenant John A. Larkin Memorial Prize.

He currently resides in Tribeca in New York City but travels extensively internationally.

Edition details

• Publisher: O'Reilly Media
• Publication date: 2019
• ISBN 10: 1492035645
• ISBN 13: 9781492035640
• Binding: Paperback
• Language: English
• Edition number: 1
• Number of pages: 362