Data Without Labels

Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems.

In Models and Algorithms for Unsupervised Learningyou'll learn:

• Fundamental building blocks and concepts of machine learning and unsupervised learning
• Data cleaning for structured and unstructured data like text and images
• Unsupervised time series clustering, Gaussian Mixture models, and statistical methods
• Building neural networks such as GANs and autoencoders
• How to interpret the results of unsupervised learning
• Choosing the right algorithm for your problem
• Deploying unsupervised learning to production
• Business use cases for machine learning and unsupervised learning

Models and Algorithms for Unsupervised Learningintroduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You'll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don't get bogged down in theory-the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. about the technology Unsupervised learning and machine learning algorithms draw inferences from unannotated data sets. The self-organizing approach to machine learning is great for spotting patterns a human might miss.