84 items
Matching Clustering to Data Shape
Slides / VideoAnomaly Detection with Unsupervised Methods
Slides / VideoAssociation Rule Learning Basics
Slides / VideoClustering Evaluation Metrics
Slides / VideoHard vs Soft Clustering
Slides / VideoGaussian Mixture Models and Soft Clustering
Slides / VideoDensity-Based Clustering: DBSCAN
Slides / VideoDendrograms and Cluster Hierarchies
Slides / VideoHierarchical Clustering Explained
Slides / VideoDistance and Similarity Metrics
Slides / VideoTypes of Unsupervised Learning Tasks
Slides / VideoCommon Applications of Clustering
Slides / VideoK-Means Limitations and Assumptions
Slides / VideoChoosing K: Elbow and Silhouette
Slides / VideoK-Means Clustering Algorithm
Slides / VideoWhat Clustering Is and Its Goal
Slides / VideoMean Shift Bandwidth Parameter
Code Quizt-SNE Cluster Visualization
Code QuizThe Elbow Method
QuizClustering Applications
QuizHard vs Soft Assignment
QuizInternal Evaluation Metrics
QuizExternal Metrics: ARI and NMI
QuizCurse of Dimensionality
QuizDBSCAN Parameters
QuizUnsupervised Anomaly Detection
QuizAgglomerative vs Divisive
QuizK-Means Limitations
QuizDBSCAN Basics
QuizHow K-Means Works
QuizChoosing a Clustering Algorithm
QuizGaussian Mixture Models
QuizDistance Metrics
QuizSilhouette Score
QuizK-Means++ Initialization
QuizReading a Dendrogram
QuizWard Linkage
QuizApriori and Support
QuizHandling Outliers
QuizCounting Points per Cluster
Code QuizPCA fit/transform Order
Code QuizPredicting Cluster for New Point
Code QuizK-Means n_init Setting
Code QuizHard vs Soft Clustering Output
Code QuizAdjusted Rand Index Arguments
Code QuizPairwise Distance Matrix
Code QuizK-Means Convergence Tolerance
Code QuizAgglomerative Merge Step
Code QuizEM E-Step vs M-Step
Code QuizSingle Linkage Distance
Code QuizDBSCAN Parameter Order
Code QuizDBSCAN Core Point Check
Code QuizGMM Responsibilities Normalization
Code QuizDBSCAN Noise Labeling
Code QuizCutting a Dendrogram
Code QuizK-Means++ Initialization
Code QuizEuclidean Distance to Centroids
Code QuizK-Means Inertia Calculation
Code QuizSilhouette Score Formula
Code QuizCosine vs Euclidean Distance
Code QuizElbow Method for K
Code QuizK-Means Update Step
Code QuizK-Means Assignment Step
Code QuizExternal Metrics and ARI
FlashcardReal-World Unsupervised Applications
FlashcardUnsupervised Anomaly Detection
FlashcardChallenges of Evaluating Unsupervised Learning
FlashcardAssociation Rule Learning (Apriori)
FlashcardInternal Cluster Evaluation Metrics
FlashcardClustering Algorithm Families
Flashcardt-SNE and UMAP
FlashcardDimensionality Reduction Overview
FlashcardDBSCAN
FlashcardDendrograms
FlashcardAgglomerative Clustering
FlashcardK-Means++ Initialization
FlashcardElbow Method
FlashcardK-Means Algorithm
FlashcardGaussian Mixture Models
FlashcardLinkage Criteria
FlashcardK-Means Limitations
FlashcardSilhouette Score
FlashcardDistance Metrics
FlashcardHard vs Soft Clustering
FlashcardCounting Points per Cluster
Find the bug in code that counts how many points fall in each cluster.
import numpy as np from sklearn.cluster import KMeans km = KMeans(n_clusters=3, n_init=10, random_state=0).fit(X) labels = km.labels_ # Count points per cluster values, counts = np.unique(labels) print(counts)
What is the bug in this code?