Unsupervised Learning & Clustering
Discover hidden patterns in unlabelled data. Master K-Means, DBSCAN, hierarchical clustering, dimensionality reduction with PCA & t-SNE, and anomaly detection.
What You'll Learn
Apply K-Means clustering to real-world segmentation problems
Use DBSCAN for density-based clustering of irregular shapes
Build and interpret dendrograms with hierarchical clustering
Reduce dimensions with PCA while preserving variance
Visualise high-dimensional data with t-SNE and UMAP
Detect anomalies using Isolation Forest and One-Class SVM
Curriculum Breakdown
Learning Format
Video Lessons
High-quality recorded lessons you can watch at your own pace.
Hands-on Projects
Real-world projects that reinforce every concept you learn.
Certificate
Earn a verifiable certificate upon successful completion.
Certification Details
Unsupervised Learning & Clustering Certificate
Issued by Tech101
Validate your ability to find structure in unlabelled data with this professional credential.
Certificate Requirements
- Shareable on LinkedIn
- PDF download
- Unique verification ID
Completion Certificate
Awarded upon finishing all course content and submitting projects. Shows dedication and completion.
Graded Certificate
Earned by passing the final assessment with 70%+ score. Demonstrates verified skill proficiency.
Your Instructor
Sarah Johnson
Data Scientist & ML Engineer
Sarah has 10 years of experience in data science and machine learning, previously at Google AI and DeepMind.
Requirements & Prerequisites
Technical Requirements
- Machine Learning Fundamentals
- Linear algebra basics (vectors, matrices) are helpful
Who This Course Is For
- ML practitioners who want to go beyond supervised learning
- Data scientists working with unlabelled or exploratory datasets
- Data Science & AI track students
Student Reviews
Frequently Asked Questions
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