Unsupervised Learning with Generative AI (MEAP V09)

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 Unsupervised Learning with Generative AI you’ll learn
Fundamental building blocks and concepts of machine learning and unsupervised learning
Data cleaning for structured and unstructured data like text and images
Clustering algorithms like kmeans, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering
Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE
Association rule algorithms like aPriori, ECLAT, SPADE
Unsupervised time series clustering, Gaussian Mixture models, and statistical methods
Building neural networks such as GANs and autoencoders
Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling
Association rule algorithms like aPriori, ECLAT, and SPADE
Working with Python tools and libraries like sklearn, bumpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, andFflask
How to interpret the results of unsupervised learning
Choosing the right algorithm for your problem
Deploying unsupervised learning to production

Unsupervised Learning with Generative AI introduces 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. You’ll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge.