Clustering Analysis

Intermediate Level
4 weeks to complete at 10 hours a week
Flexible Schedule

Di Wu

What You’ll Learn

Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction.

Apply clustering techniques to diverse datasets for pattern discovery and data exploration.

Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space.

Skills You’ll Gain

Exploratory Data Analysis Applied Machine Learning Data analysis Statistical Machine Learning Machine Learning Algorithms Scikit Learn (Machine Learning Library) Unsupervised Learning Machine Learning Dimensionality Reduction Machine Learning Methods

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There are 6 modules in this course

This week provides an introduction to unsupervised learning and clustering analysis. You will delve into partitioning clustering methods, such as K-Means and K-Medoids, understanding their principles and applications.

This week you will explore hierarchical clustering, a method that creates a tree-like structure to represent data similarities.

This week focuses on density-based clustering, which groups data points based on their density within the dataset.

Throughout this week, you will explore grid-based clustering, an approach that partitions the data space into grids for efficient clustering.

This week introduces dimension reduction techniques as a critical preprocessing step for handling high-dimensional data.

The final week focuses on a comprehensive case study where you will apply clustering and dimension reduction techniques to solve a real-world problem.