Unsupervised Learning | Clustering, Dimensionality reduction, Case Study | Full Chapter 5(3 Lessons)
About this lesson
Dive into the world of Unsupervised Learning and understand how machines uncover hidden patterns without labeled data. In this video, we break down two major techniques: 🔹 Clustering K-Means DBSCAN Hierarchical Clustering 🔹 Dimensionality Reduction PCA (Principal Component Analysis) t-SNE (t-Distributed Stochastic Neighbor Embedding) UMAP (Uniform Manifold Approximation and Projection) We also walk through a practical case study to show how these techniques are applied in real-world scenarios. Whether you're a beginner in Machine Learning or brushing up your concepts, this video will give you a clear and intuitive understanding of unsupervised learning methods. This video combines all 3 lessons of Chapter 5 (Unsupervised Learning) from Machine Learning Essentials Part 1: Clustering (K-means, DBSCAN, Hierarchical) Part 2: Dimensionality reduction (PCA, t-SNE, UMAP) Part 3: Case Study : Customer segmentation in marketing 👉 Watch each part separately here: - Part 1: https://youtu.be/sZ24TduG9vI - Part 2: https://youtu.be/5qxvhR_p4Z4 - Part 3: https://youtu.be/1UXQ0vfV1n4 Playlist: https://www.youtube.com/playlist?list=PLidUwI_DLURZzUhuYcqgxUd6hAuDVYLnI ❤️ Support the Channel 👍 Like | 💬 Comment | 🔔 Subscribe to @PracticalAIPro to master Artificial Intelligence — step-by-step, concept-by-concept!
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