Description
Cluster Analysis and Unsupervised Machine Learning in Python
Not all data comes with labels—but that doesn’t mean it’s useless. This course teaches you to uncover hidden structures in unlabeled data using **K-Means, Hierarchical Clustering, Gaussian Mixture Models (GMMs), and PCA**—with real-world applications in customer segmentation, anomaly detection, and data compression.
What You’ll Build
- A customer segmentation engine for e-commerce using K-Means
- An anomaly detector for server logs using GMMs
- A dimensionality reducer for visualizing high-dimensional data in 2D
- A hierarchical clustering system for biological taxonomy or document grouping
Algorithms You’ll Master
- K-Means Clustering—centroid-based grouping with elbow method & silhouette analysis
- Hierarchical Clustering—dendrograms, agglomerative vs divisive
- Gaussian Mixture Models (GMMs)—soft clustering with probabilistic assignments
- Principal Component Analysis (PCA)—linear dimensionality reduction
- t-SNE—non-linear visualization for complex manifolds
Why Unsupervised Learning?
- 90% of data is unlabeled—master the tools to extract value from it
- Preprocessing for supervised tasks—clustering often reveals features that boost model performance
- Business-critical use cases—market segmentation, fraud detection, exploratory data analysis
Who Is This For?
- Data scientists tired of only using labeled datasets
- Students needing unsupervised learning projects for portfolios
- Analysts exploring customer or product grouping
- Engineers building anomaly detection systems
From Raw Data to Insight—Without Labels
You’ll graduate with a toolkit of **unsupervised techniques** that reveal patterns even when you don’t know what to look for.
Ready to find hidden structure in your data? Enroll now.
