Machine Learning for Data Analysis: Unsupervised Learning

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 ProfilingPART 2: Classification ModelingPART 3: Regression & ForecastingPART 4: Unsupervised LearningThis 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 LearningUnsupervised Learning LandscapeCommon Unsupervised TechniquesFeature EngineeringThe Unsupervised ML WorkflowSection 2: Clustering & SegmentationClustering BasicsK-Means ClusteringWSS & Elbow PlotsHierarchical ClusteringInterpreting a DendogramSection 3: Association MiningAssociation Mining BasicsThe Apriori AlgorithmBasket AnalysisMinimum Support ThresholdsInfrequent & Multiple Item SetsMarkov ChainsSection 4: Outlier DetectionOutlier Detection BasicsCross-Sectional OutliersNearest NeighborsTime-Series OutliersResidual DistributionSection 5: Dimensionality ReductionDimensionality Reduction BasicsPrinciple Component Analysis (PCA)Scree PlotsAdvanced TechniquesThroughout 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 videoMachine Learning: Unsupervised Learning ebookDownloadable Excel project fileExpert Q & A forum30-day money-back guaranteeHappy learning.