Unsupervised Learning Explained | Clustering & Gaussian Mixtures | Hands-On ML Chapter 9 скачать в хорошем качестве

Unsupervised Learning Explained | Clustering & Gaussian Mixtures | Hands-On ML Chapter 9

In this session, we explore Unsupervised Learning — where models discover patterns without labeled data. This video covers Chapter 9 of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, diving into clustering, density estimation, and anomaly detection. We discuss: What unsupervised learning really means Clustering fundamentals K-Means and its limitations Choosing the number of clusters Using clustering for preprocessing and semi-supervised learning DBSCAN and density-based clustering Gaussian Mixture Models (GMMs) Anomaly detection using probabilistic models Bayesian Gaussian Mixtures When clustering fails (and why) Unlike supervised learning, there are no labels guiding the model. The structure must be inferred from the data itself — and that makes evaluation much more subtle. As always, this is a reading-group style discussion focused on intuition, assumptions, and trade-offs — not just running algorithms. Who this video is for: Beginners building strong ML foundations Engineers curious how clustering works in practice Students confused about K-Means vs DBSCAN vs GMM Anyone interested in anomaly detection and unsupervised patterns 📌 Series: Hands-On Machine Learning Reading Group 📌 Chapter: 9 📌 Focus: Unsupervised Learning Techniques Welcome to Kwargs AI Labs — where patterns are discovered, not predefined.