Machine Learning for Data Analysis: Unsupervised Learning

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HEADS UP! This course is now part of The Complete Visual Guide to Machine Learning & Data Science, which combines all 4 Machine Learning courses from Maven Analytics. This course, along with the other individual courses in the series, will be retired soon. This course is PART 4 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning: PART 1: QA & Data Profiling PART 2: Classification Modeling PART 3: Regression & Forecasting PART 4: Unsupervised Learning This course makes data science approachable to everyday people, and is designed to demystify powerful Machine Learning tools & techniques without trying to teach you a coding language at the same time. Instead, we’ll use familiar, user-friendly tools like Microsoft Excel to break down complex topics and help you understand exactly HOW and WHY machine learning works before you dive into programming languages like Python or R. Unlike most Data Science and Machine Learning courses, you won’t write a SINGLE LINE of code. COURSE OUTLINE: In this course, 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 language to help you build an intuition for how these models actually work, from K-Means and Apriori to outlier detection, Principal Component Analysis, and more. Section 1: Intro to Unsupervised Machine Learning Unsupervised Learning Landscape Common Unsupervised Techniques Feature Engineering The Unsupervised ML Workflow Section 2: Clustering & Segmentation Clustering Basics K-Means Clustering WSS & Elbow Plots Hierarchical Clustering Interpreting a Dendogram Section 3: Association Mining Association Mining Basics The Apriori Algorithm Basket Analysis Minimum Support Thresholds Infrequent & Multiple Item Sets Markov Chains Section 4: Outlier Detection Outlier Detection Basics Cross-Sectional Outliers Nearest Neighbors Time-Series Outliers Residual Distribution Section 5: Dimensionality Reduction Dimensionality Reduction Basics Principle Component Analysis (PCA)Scree Plots Advanced Techniques Throughout the course, we’ll introduce unique demos and real-world case studies to help solidify key concepts along the way. You’ll see how k-means can help identify customer segments, how apriori 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! Join today and get immediate, lifetime access to the following: High-quality, on-demand video Machine Learning: Unsupervised Learning ebook Downloadable Excel project file Expert Q & A forum30-day money-back guarantee Happy learning!-Josh M. (Lead Machine Learning Instructor, Maven Analytics) Looking for our full business intelligence stack? Search for Maven Analytics to browse our full course library, including Excel, Power BI, My SQL, and Tableau courses! See why our courses are among the TOP-RATED on Udemy: Some of the BEST courses I’ve ever taken. I’ve studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I’ve seen! Russ C. This is my fourth course from Maven Analytics and my fourth 5-star review, so I’m running out of things to say. I wish Maven was in my life earlier! Tatsiana M. Maven Analytics should become the new standard for all courses taught on Udemy! Jonah M.