Machine Learning for Data Analysis Unsupervised Learning

Machine Learning made simple with Excel! Unsupervised learning topics for advanced data analysis & business intelligence
Machine Learning for Data Analysis Unsupervised Learning
File Size :
596.40 MB
Total length :
2h 0m

Category

Instructor

Maven Analytics

Language

Last update

Last updated 11/2022

Ratings

4.6/5

Machine Learning for Data Analysis Unsupervised Learning

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

Machine Learning for Data Analysis Unsupervised Learning

Requirements

This is a beginner-friendly course (no prior knowledge or math/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 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!-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.

Overview

Section 1: Getting Started

Lecture 1 Course Structure & Outline

Lecture 2 READ ME: Important Notes for New Students

Lecture 3 About This Series

Lecture 4 DOWNLOAD: Course Resources

Lecture 5 Setting Expectations

Section 2: Intro to Unsupervised ML

Lecture 6 Supervised vs. Unsupervised Learning

Lecture 7 Common Unsupervised Techniques

Lecture 8 Unsupervised ML Workflow

Lecture 9 Feature Engineering

Lecture 10 KEY TAKEAWAYS: Intro to Unsupervised ML

Section 3: Clustering & Segmentation

Lecture 11 Introduction

Lecture 12 Clustering Basics

Lecture 13 Intro to K-Means

Lecture 14 WSS & Elbow Plots

Lecture 15 K-Means FAQs

Lecture 16 CASE STUDY: K-Means

Lecture 17 Intro to Hierarchical Clustering

Lecture 18 Anatomy of a Dendrogram

Lecture 19 Hierarchical Clustering FAQs

Lecture 20 KEY TAKEAWAYS: Clustering & Segmentation

Section 4: Association Mining & Basket Analysis

Lecture 21 Introduction

Lecture 22 Association Mining Basics

Lecture 23 The Apriori Algorithm

Lecture 24 Basket Analysis Examples

Lecture 25 Minimum Support Thresholds

Lecture 26 Infrequent Itemsets

Lecture 27 Multiple Item Sets

Lecture 28 CASE STUDY: Apriori

Lecture 29 Markov Chains

Lecture 30 CASE STUDY: Markov Chains

Lecture 31 KEY TAKEAWAYS: Association Mining

Section 5: Outlier Detection

Lecture 32 Introduction

Lecture 33 Outlier Detection Basics

Lecture 34 Cross-Sectional Outliers

Lecture 35 Cross-Sectional Outlier Example

Lecture 36 CASE STUDY: Cross-Sectional Outlier

Lecture 37 Time-Series Outliers

Lecture 38 Time-Series Outlier Example

Lecture 39 KEY TAKEAWAYS: Outlier Detection

Section 6: Dimensionality Reduction

Lecture 40 Introduction

Lecture 41 Dimensionality Reduction Basics

Lecture 42 Principle Component Analysis

Lecture 43 PCA Example

Lecture 44 Interpreting Components

Lecture 45 Scree Plots

Lecture 46 Advanced Techniques

Lecture 47 KEY TAKEAWAYS: Dimensionality Reduction

Section 7: Wrapping Up

Lecture 48 Series Conclusion

Lecture 49 BONUS LESSON

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

Course Information:

Udemy | English | 2h 0m | 596.40 MB
Created by: Maven Analytics

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