Applied Unsupervised Learning with Python
Starting with the basics, Applied Unsupervised Learning with Python explains various techniques that you can apply to your data using the powerful Python libraries so that your unlabeled data reveals solutions to all your business questions.
Offered by
Difficulty Level
Intermediate
Completion Time
16h4m
Language
English
About Book
Who Is This Book For?
This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.
Applied Unsupervised Learning with Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 16h4m total length
Introduction to Clustering
Hierarchical Clustering
Neighborhood Approaches and DBSCAN
An Introduction to Dimensionality Reduction and PCA
Autoencoders
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Topic Modeling
Market Basket Analysis
Hotspot Analysis
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