Applied ML II: Unsupervised & Specialized Methods

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In this course, you will learn how to uncover structure, patterns, and insights in data using advanced unsupervised and specialized machine learning techniques. You’ll apply clustering algorithms to segment customers or behaviors, detect anomalies using modern outlier-detection methods, reduce high-dimensional data with PCA, UMAP, and t-SNE, analyze text through core NLP techniques, build reinforcement learning agents for sequential decision-making, and create accurate time series forecasts. By completing this course, you will gain a deep, practical understanding of ML methods that go beyond traditional supervised learning—skills that are increasingly essential in real-world data science roles. You’ll learn not only how these algorithms work, but also when to use them, how to evaluate them, and how they connect to broader business and analytical objectives. What makes this course unique is its hands-on, multi-perspective approach: you’ll explore algorithms through multiple real-world contexts—including clustering, text analysis, RL environments, and forecasting tasks—giving you a versatile and intuitive grasp of modern ML. Whether you aim to expand your machine learning toolkit or solve more complex analytical problems, this course will guide you toward becoming a more capable and confident practitioner.

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