AI FundamentalsUnsupervised Learning and Clustering

84 items

1

Matching Clustering to Data Shape

Slides / Video
2

Anomaly Detection with Unsupervised Methods

Slides / Video
3

Association Rule Learning Basics

Slides / Video
4

Clustering Evaluation Metrics

Slides / Video
5

Hard vs Soft Clustering

Slides / Video
6

Gaussian Mixture Models and Soft Clustering

Slides / Video
7

Density-Based Clustering: DBSCAN

Slides / Video
8

Dendrograms and Cluster Hierarchies

Slides / Video
9

Hierarchical Clustering Explained

Slides / Video
10

Distance and Similarity Metrics

Slides / Video
11

Types of Unsupervised Learning Tasks

Slides / Video
12

Common Applications of Clustering

Slides / Video
13

K-Means Limitations and Assumptions

Slides / Video
14

Choosing K: Elbow and Silhouette

Slides / Video
15

K-Means Clustering Algorithm

Slides / Video
16

What Clustering Is and Its Goal

Slides / Video
17

t-SNE Cluster Visualization

Code Quiz
18

Mean Shift Bandwidth Parameter

Code Quiz
19

Agglomerative vs Divisive

Quiz
20

DBSCAN Parameters

Quiz
21

DBSCAN Basics

Quiz
22

How K-Means Works

Quiz
23

Reading a Dendrogram

Quiz
24

Handling Outliers

Quiz
25

Distance Metrics

Quiz
26

Unsupervised Anomaly Detection

Quiz
27

Clustering Applications

Quiz
28

Curse of Dimensionality

Quiz
29

External Metrics: ARI and NMI

Quiz
30

Internal Evaluation Metrics

Quiz
31

Choosing a Clustering Algorithm

Quiz
32

Silhouette Score

Quiz
33

Gaussian Mixture Models

Quiz
34

K-Means++ Initialization

Quiz
35

Ward Linkage

Quiz
36

Apriori and Support

Quiz
37

K-Means Limitations

Quiz
38

Hard vs Soft Assignment

Quiz
39

The Elbow Method

Quiz
40

Counting Points per Cluster

Code Quiz
41

Adjusted Rand Index Arguments

Code Quiz
42

Hard vs Soft Clustering Output

Code Quiz
43

PCA fit/transform Order

Code Quiz
44

Predicting Cluster for New Point

Code Quiz
45

K-Means n_init Setting

Code Quiz
46

Pairwise Distance Matrix

Code Quiz
47

K-Means Convergence Tolerance

Code Quiz
48

Agglomerative Merge Step

Code Quiz
49

DBSCAN Core Point Check

Code Quiz
50

EM E-Step vs M-Step

Code Quiz
51

Single Linkage Distance

Code Quiz
52

DBSCAN Parameter Order

Code Quiz
53

GMM Responsibilities Normalization

Code Quiz
54

DBSCAN Noise Labeling

Code Quiz
55

Cutting a Dendrogram

Code Quiz
56

Elbow Method for K

Code Quiz
57

Silhouette Score Formula

Code Quiz
58

K-Means++ Initialization

Code Quiz
59

Euclidean Distance to Centroids

Code Quiz
60

K-Means Inertia Calculation

Code Quiz
61

Cosine vs Euclidean Distance

Code Quiz
62

K-Means Update Step

Code Quiz
63

K-Means Assignment Step

Code Quiz
64

External Metrics and ARI

Flashcard
65

Real-World Unsupervised Applications

Flashcard
66

Unsupervised Anomaly Detection

Flashcard
67

Challenges of Evaluating Unsupervised Learning

Flashcard
68

Association Rule Learning (Apriori)

Flashcard
69

Internal Cluster Evaluation Metrics

Flashcard
70

Clustering Algorithm Families

Flashcard
71

t-SNE and UMAP

Flashcard
72

Dimensionality Reduction Overview

Flashcard
73

K-Means Algorithm

Flashcard
74

Gaussian Mixture Models

Flashcard
75

Linkage Criteria

Flashcard
76

K-Means Limitations

Flashcard
77

Silhouette Score

Flashcard
78

Distance Metrics

Flashcard
79

Hard vs Soft Clustering

Flashcard
80

DBSCAN

Flashcard
81

Dendrograms

Flashcard
82

Agglomerative Clustering

Flashcard
83

K-Means++ Initialization

Flashcard
84

Elbow Method

Flashcard
Code QuizIntermediate

Silhouette Score Formula

Spot the reversed terms in this silhouette calculation.

Codepython
def silhouette(a, b):
    # a = mean intra-cluster distance
    # b = mean nearest-cluster distance
    return (a - b) / max(a, b)

What is the bug in this silhouette-score formula?