Machine Learning for Data Analysis: Unsupervised Learning

Build foundational Machine Learning data science skills WITHOUT writing complex codeUse intuitive, user-friendly tools like Microsoft Excel to introduce demystify machine learning tools techniquesExplore powerful techniques for clustering, association mining, outlier detection, and dimensionality reductionLearn how ML models like K-Means, Apriori, Markov and Principal Component Analysis actually workEnjoy unique, hands-on demos to see how Unsupervised ML can be applied to real-world Business Intelligence projectsThis 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 QA forum30-day money-back guaranteeHappy 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, MySQL, 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.Who this course is for:Anyone looking to learn the basics of machine learning through real-world demos and intuitive, crystal clear explanationsData Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learningR or Python users seeking a deeper understanding of the models and algorithms behind their codeAnalytics professionals who want to learn powerful tools for clustering, association mining, basket analysis and outlier detection