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- AI
Unsupervised Learning
This learning path is actively in production. More content will be added as it becomes available in the library. Planned content includes: - Foundations of Unsupervised Learning (video course) - Clustering in Practice (video course) - Dimensionality Reduction: Linear Methods (video course) - Nonlinear Dimensionality Reduction and Visualization (video course) - Anomaly Detection and Unstructured Data Applications (video course) - Building Systems with Unsupervised Learning (video course) - Lab: Clustering and Dimensionality Reduction (hands-on lab) - Anomaly Detection Pipeline (hands-on lab)
Unsupervised learning is a branch of machine learning used to discover patterns, structure, and relationships in unlabeled data. It powers applications such as customer segmentation, anomaly detection, and semantic search. This path covers applied unsupervised learning techniques including clustering, dimensionality reduction, anomaly detection, and embeddings, with a strong focus on real-world workflows and modern systems. Learners will build practical skills using tools such as PCA, UMAP, Isolation Forest, and vector search to design and evaluate production-ready solutions.
Content in this path
Unsupervised Learning
Watching the following courses to get learning about Unsupervised Learning!
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What You'll Learn
- How to identify when to use unsupervised learning and prepare data for modeling
- How to discover patterns using clustering and dimensionality reduction techniques
- How to detect anomalies and extract insights from structured and unstructured data
- How to work with embeddings and build semantic search and recommendation systems
- How to combine unsupervised methods into real-world, production-ready workflows
- Learners should have a basic understanding of Python and common data science libraries such as NumPy, pandas, and scikit-learn, along with familiarity in working with tabular or text data. Foundational knowledge of machine learning concepts (e.g., supervised learning, model evaluation) and basic linear algebra and statistics will be helpful but not required.
- Machine Learning
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