Clustering Analysis
Instructor: Di Wu
Intermediate Level • 4 weeks to complete at 10 hours a week • Flexible Schedule
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
Shareable Certificate
Earn a shareable certificate to add to your LinkedIn profile
Outcomes
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
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.