fbpx

Unraveling the Mysteries of Unsupervised Learning πŸ•΅οΈβ€β™‚οΈ

Welcome to unsupervised learning, where algorithms find hidden patterns in data without labels! 🀯 In this post, let’s explore clustering – a key technique. 🧐

What is it? πŸ’­

Unsupervised learning helps uncover structures in data without guidance. It’s like a puzzle without a box image!

Clustering 101 πŸ“š

Clustering groups similar data points into clusters to maximize similarities within and minimize between clusters. πŸ‘₯πŸ‘₯πŸ‘₯

It’s like sorting a bag of marbles by colour without knowing the colours first! 🌈

Types of Clustering πŸ“ˆ

🌟 K-Means: Partitions data into ‘k’ clusters based on means. πŸ“Š

🌟 Hierarchical: Builds a tree of clusters. πŸ‘¨β€πŸ‘©β€πŸ‘§β€πŸ‘¦

🌟 DBSCAN: Groups close points mark outliers. πŸ“πŸ“πŸ“

🌟 GMM: Assumes data comes from Gaussian mixtures. πŸ“ˆ

Real-World Uses 🏒

πŸš€ Customer segmentation

πŸš€ Image segmentation

πŸš€ Anomaly detection

πŸš€ NLP

Evaluating Quality 🧐

Silhouette scores, the Davies-Bouldin index and visual inspection help assess cluster quality.

Challenges 🚧

🚧 Choosing the number of clusters

🚧 Scalability with extensive data

🚧 Interpretability

The world of unsupervised learning is fascinating yet complex! I hope this quick intro to clustering piques your interest. 😊 Let me know your thoughts!

Follow me on Linkedin and Twitter and join me on my quest through realms of #AI, #DataEngineering, #dataanalytics, #MachineLearning, #CRMMarketing, #SoftwareEngineering and #Web3 as we unravel the mysteries of innovation together! πŸŒŸπŸš€

Machine Learning Clustering 101: Unravel the Mysteries of Unsupervised Learning