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Cluster Analysis with Python
Introduction
Welcome! (4:24)
Cluster Analysis with Copilot in Excel (11:33)
Installing Python on Windows (9:26)
Installing Python on Mac (9:00)
Introducing Cluster Analysis (13:36)
Iris Dataset (5:36)
Hand-Written Digits Dataset (3:30)
Heart Dataset (2:47)
Cluster Analysis Fundamentals
Clustering Algorithms (12:34)
Cluster Types (5:45)
K-Means Clustering
The K-Means Algorithm (3:38)
K-Means by Example (8:35)
K-Means Math (7:43)
Dealing with Outliers (2:56)
K-Means with Python (8:13)
Visualizing the Clusters (8:35)
Standardizing the Data (2:58)
Real-World K-Means (4:10)
Hands-On Lab #1
Lab Instructions & Files
Optimizing K-Means
Evaluating Clusters (3:50)
Calculating Cohesion (3:20)
Evaluating Cohesion (3:17)
The Elbow Method (4:24)
The Silhouette Coefficient (9:38)
Hands-On Lab #2
Lab Instructions & Files
DBSCAN Clustering
The DBSCAN Algorithm (5:31)
DBSCAN by Example (7:08)
DBSCAN with Python (1:38)
DBSCAN Results (1:58)
Real-World DBSCAN (2:34)
Optimizing DBSCAN
DBSCAN Hyperparameters (3:05)
Choosing eps (3:23)
Finding Nearest Neighbors (5:56)
eps Elbow Method (4:14)
Optimized DBSCAN Results (4:55)
K-Means vs. DBSCAN (2:17)
Hands-On Lab #3
Lab Instructions & Files
Dimensionality Reduction
Dimensionality Reduction Intuition (4:23)
Principal Component Analysis (PCA) (10:30)
PCA with Python (4:18)
Hands-On Lab #4
Lab Instructions & Files
Categorical Data
The Problem with Categories (1:58)
One-Hot Encoding (7:15)
Factor Analysis of Mixed Data (9:16)
FAMD with Python (8:08)
Hands-On Lab #5
Lab Instructions & Files
Cluster Analysis Case Study
Handling Missing Data (6:43)
Loading the Data (6:23)
Profiling the Data (10:09)
Standardizing the Data (2:16)
Optimizing K-Means (7:49)
Perform the Clustering (3:32)
Interpretation Round 1 (22:00)
Predictive Modeling (9:36)
Interpretation Round 2 (11:24)
PCA the Data (8:02)
Course Wrap-Up
Continue Your Learning (2:26)
I Need Your Help (1:18)
Finding Nearest Neighbors
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