With a subtitle of "How to Build Applied Machine Learning Solutions from Unlabeled Data", this book shows how 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 hands-on examples and code. He shows how to identify difficult-to-find patterns in data, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets.
Author: Ankur A. Patel
Date: March 2019
Audience: Python developers interested in machine learning
Category: Artificial Intelligence and Python
- 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
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