Link to Content:

DataCamp Unsupervised Learning in Python

Created/Published/Taught by:

DataCamp

Benjamin Wilson

Hugo Bowne-Anderson

Yashas Roy

Content Found Via:

DataCamp

Free? Partially: Some Free Content, Some Paid

Cost Range:

$0.00 - $29.00

Tags: clustering / dimension reduction / machine learning / matrix factorization / python / scikit-learn / scipy / unsupervised learning

Difficulty Rating:

FromÂ DataCamp:

“Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you’ll learn the fundamental of unsupervised learning and implement the essential algorithms using scikit-learn and scipy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.”

This course consists of four chapters:

- Clustering for dataset exploration
- Visualization with hierarchical clustering and t-SNE
- Decorrelating your data and dimension reduction
- Discovering interpretable features

Recommended Prerequisites: Intro to Python for Data Science, Intermediate Python for Data Science, Statistical Thinking in Python (Part 1)

Go to Content: DataCamp – Unsupervised Learning in Python

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