Clustering and Dimensionality Reduction

What you'll learn

👉 K-means based clustering (k-means, k-modes, k-prototypes).

👉 Agglomerative clustering and linkage methods (including Min, Max, Average and Wald).

👉 Density based clustering (DBSCAN, HDBSCAN).

👉 Graph based clustering (Louvain algorithm).

👉 Clustering quality metrics, including DBCV, silhouette scores and graph clustering metrics.

👉 Clustering numerical, categorical and graph data.

👉 PCA dimensionality reduction.

👉 UMAP dimensionality reduction.

👉 Evaluating clustering quality with UMAP.

👉 Data preprocessing methods for clustering and dimensionality reduction (including, data scaling, handling skewed data, encoding categorical data, calculating distance metrics).

👉 Data preprocessing methods to create graphs from data (including KNN and SNN approaches).

👉 Python prerequisites.

What you'll get

💬 English subtitles

Instructor

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Clustering and Dimensionality Reduction Course

Welcome to the definitive course on unsupervised machine learning—designed to go deeper than any other resource online.

While platforms like Udemy and Coursera offer introductory content, this course delivers unmatched depth, combining rigorous theory, hands-on implementation, and real-world case studies you won’t find elsewhere.

Why This Course Stands Apart

✅ No fluff, no shortcuts – We dissect every critical algorithm, from foundational concepts to advanced optimizations.
✅ Beyond the basics – Most courses stop at K-means and PCA; we cover hierarchical clustering, DBSCAN, HDBSCAN, UMAP, and more.
✅ Real-world rigor – Apply techniques to complex datasets (like RNA profiling, geospacial clustering, clustering customers and clustering actors) that mirror cutting-edge industry challenges.
✅ Code + theory + intuition – Not just toy examples—you’ll build production-ready solutions.

This isn’t just another overview—it’s the deepest dive into unsupervised learning available online.

Why Unsupervised Learning

Unsupervised learning unlocks hidden patterns and structures in data (a process known as data mining) without relying on pre-labeled examples. This approach isn’t just useful—it’s often essential when labeling data is impractical or impossible.

In this course, we’ll focus on two transformative techniques:

• Cluster analysis – Groups similar data points, revealing underlying patterns.
• Dimensionality reduction – Reduces the number of features to simplify analysis, improve algorithm performance, and uncover meaningful structure.

Mastering these methods is key to extracting actionable insights—a must-have skill in data science.

Why It Matters

These techniques power real-world applications across industries:

• Marketing: Customer segmentation and behavior analysis.
• Healthcare: Disease pattern detection and patient profiling.
• Bioinformatics: Genetic data interpretation.
  
• Social Networks: Community structure analysis.
• Urban Planning: Traffic and infrastructure optimization.

What You’ll Learn

We’ll break down unsupervised learning algorithms—exploring how they work, their strengths, and their limitations. But we won’t stop at theory. You’ll implement them yourself through:

• Hands-on demonstrations
• Targeted case studies (each reinforcing key concepts)
• A capstone project: Clustering cells using RNA profiles—a real-world example of extracting insights from complex data.

By the end, you’ll have the skills to apply these techniques in your own projects. Whether you’re a practicing data scientist or a curious learner, this course will deepen your understanding of machine learning’s unsupervised frontier.

Who is this course for

From zero to hero—no prior expertise required.

• Beginners? We’ve got you. Dedicated Python primers will get you up to speed fast.
• Advanced learners? Skip straight to clustering and dimensionality reduction—then apply your skills immediately.

We designed this course so that even with minimal Python experience, you'll finish with the ability to analyze real data using clustering and dimensionality reduction—while advanced learners can dive straight into practical applications.

Frequently Asked Questions

When does the course begin and end?

You can start taking the course from the moment you enroll. The course is self-paced, so you can watch the tutorials and apply what you learn whenever you find it most convenient.

For how long can I access the course?

The course has lifetime access. This means that once you enroll, you will have unlimited access to the course for as long as you like.

What if I don't like the course?

There is a 30-day money back guarantee. If you don't find the course useful, contact us within the first 30 days of purchase and you will get a full refund.

Will I get a certificate?

Yes, you'll get a certificate of completion after completing all lectures, quizzes and assignments.

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