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

• PART2:Classification Modeling

• PART3:Regression & Forecasting

• PART4: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 SINGLELINEof code.

COURSEOUTLINE:

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!

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Join todayand get immediate, lifetime accessto the following:

• High-quality, on-demand video

• Machine Learning:Unsupervised Learningebook

• DownloadableExcel project file

• ExpertQ&Aforum

• 30-day money-back guarantee

Happy learning!

-Josh M. (Lead Machine Learning Instructor, Maven Analytics)

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