Machine Learning for Data Analysis Unsupervised Learning

Machine Learning made simple with Excel! Unsupervised learning topics for advanced data analysis & business intelligence
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 Modeling

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.

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.