Machine Learning 4: Unsupervised Learning

This badge is earned by successfully completing the Machine Learning 4: Unsupervised Learning course at Maven Analytics. COURSE HOURS: 3.5 COURSE DESCRIPTION: This course is PART 4 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning: 1. QA & Data Profiling 2. Classification 3. Regression & Forecasting 4. Unsupervised Learning We’ll start by reviewing the Machine Learning landscape, exploring the differences between supervised and unsupervised learning, and introducing several of the most common unsupervised techniques, including cluster analysis, association mining, outlier detection, and dimensionality reduction. Throughout the course, we'll focus on breaking down each concept in plain and simple terms to help you build an intuition for how these models actually work, from K-means and hierarchical clustering to Apriori algorithms and outlier detection methods. As always, we'll introduce unique demos to solidify key concepts and tie them back to real-world case studies. You'll see how K-Means can help identify customer segments, how apriori models and Markov chains can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets. If you’re ready to build the foundation for a successful career in data science, this is the course for you.