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Description of  Machine Learning for BI, PART 4 Unsupervised Learning

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 49 lectures (2h) | Size: 528.4 MB stats background required) We'll use Microsoft Excel (Office 365) for some course demos, but participation is optional This is PART 4 of our Machine Learning for BI series (we recommend taking Parts 1, 2 & 3 first) Description 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 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. 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 Descriptions 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 Chainsmachine-learning-for-bi-part-4 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 courseAnalysis (PCA) Scree Descriptions 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! -Josh M. (Lead Machine Learning Instructor, Maven Analytics) ____www.udemy.com______ 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 explanations Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning R or Python users seeking a deeper understanding of the models and algorithms behind their code Analytics professionals who want to learn powerful tools for clustering, association mining, basket analysis and outlier detection Homepage https: What you'll learn Build foundational Machine Learning & data science skills WITHOUT writing complex code Use intuitive, user-friendly tools like Microsoft Excel to introduce & demystify machine learning tools & techniques Explore powerful techniques for clustering, association mining, outlier detection, and dimensionality reduction Learn how ML models like K-Means, Apriori, Markov and Principal Component Analysis actually work Enjoy unique, hands-on demos to see how Unsupervised ML can be applied to real-world Business Intelligence projects Requirements This is a beginner-friendly course (no prior knowledge or math