Published Apr 4, 2022

K-Means in Practice

Explore the practical application of K-Means clustering as data scientist Mujtaba Anwer delves into feature engineering, communication strategies with stakeholders, and real-world use cases like fraud detection and customer segmentation, revealing both the power and pitfalls of this essential algorithm.

Episode Highlights

Topics covered

Episode Highlights

Related Episodes

k-means clustering
Answers 383 questions
Customer Clustering
Answers 383 questions
Power K-Means
Answers 383 questions
Explainable K-Means
Answers 383 questions
k-means Image Segmentation
Answers 383 questions
Quantum K-Means
Answers 383 questions
[MINI] k-means clustering
Answers 383 questions
Matrix Factorization For k-Means
Answers 383 questions
Breathing K-Means
Answers 383 questions
Fair Hierarchical Clustering
Answers 383 questions
Applying k-Nearest Neighbors to Time Series
Answers 383 questions
ML Ops Best Practices
Answers 383 questions
Machine Learning Done Wrong
Answers 383 questions
Network Analysis in Practice
Answers 383 questions
Interpretability Practitioners
Answers 383 questions

Dexa/Kyle Polich

K-Means in Practice

Topics covered

Popular Clips

Value from Clustering

Uncovering Data Value

Unconventional Fraud Clustering

ClearML Empowers ML Teams

Cluster Analysis Insights

Cybersecurity Insights

Mastering Clustering Techniques

Clustering for Insights

Data Insights

Innovative CRM Clustering

Feature Engineering in Clustering

Decision Making Dilemma

Episode Highlights

Data Preparation

Business Integration

Clustering Techniques

Related Episodes

k-means clustering

Customer Clustering

Power K-Means

Explainable K-Means

k-means Image Segmentation

Quantum K-Means

[MINI] k-means clustering

Matrix Factorization For k-Means

Breathing K-Means

Fair Hierarchical Clustering

Applying k-Nearest Neighbors to Time Series

ML Ops Best Practices

Machine Learning Done Wrong

Network Analysis in Practice

Interpretability Practitioners

K-Means in Practice

Topics covered

Popular Clips

Episode Highlights

Data PreparationMujtaba Anwer discusses the intricacies of feature engineering and exploratory data analysis in clustering. He shares insights on overcoming challenges and effectively communicating results to stakeholders.

Data Preparation

Business Integration

Clustering TechniquesMujtaba Anwer, a data scientist, explores the practical applications of K-Means clustering in real-world scenarios. He shares insights on using this algorithm for fraud detection and customer segmentation, highlighting both its potential and limitations.

Clustering Techniques

Related Episodes