Lesson 1
Clustering
Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
Course
In this course, you'll learn how to apply unsupervised learning to solve real-world problems.
In this course, you'll learn how to apply unsupervised learning to solve real-world problems.
Intermediate
3 weeks
Real-world Projects
Completion Certificate
Last Updated May 19, 2024
Skills you'll learn:
Prerequisites:
Lesson 1
Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
Lesson 2
We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).
Lesson 3
In this lesson, we discuss Gaussian mixture model clustering. We then talk about the cluster analysis process and how to validate clustering results.
Lesson 4
Often we need to reduce a large number of features in our data to a smaller, more relevant set. Principal Component Analysis, or PCA, is a method of feature extraction and dimensionality reduction.
Lesson 5
In this lesson, we will look at two other methods for feature extraction and dimensionality reduction: Random Projection and Independent Component Analysis (ICA).
Lesson 6 • Project
In this project, you'll apply your unsupervised learning skills to two demographics datasets, to identify segments and clusters in the population, and see how customers of a company map to them.
Staff Data Scientist
Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.
Instructor
Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.
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