Thread by @pvergadia

👋 Day 5 of #31DaysofML

⚡️Unsupervised learning ⚡️
Input data is unlabeled & the program learns to recognize the inherent patterns in the input data

Eg: Data across few people's eating habits
🔸Model input = 🍏🥦🧅🍓🥞🥖🍚
🔸Model output = cluster of vegetarian/vegan

A 🧵

2/8
When is unsupervised learning used?
🔸 On large datasets where annotating (labeling) data is costly
🔸 When we don't know how many classes might exist in the data
🔸 Cluster the data to apply classification on the individual clusters

#31DaysofML

@quaesita has demystified unsupervised learning here 👉 bit.ly/quaesita_unsup…

@quaesita 3/8
Common types of unsupervised learning:
🔹 Clustering - divide the data by similarity
📌 Eg: Target marketing, Customer recommendation
🔹 Dimensionality reduction - Find wider dependencies
📌 Eg: Big data visualizations, structure discovery

#31DaysofML

@quaesita 4/8
Most used clustering algorithms:
👉 Centroid-based Clustering - k-means
👉 Density-based Clustering
👉 Distribution-based Clustering - Gaussian distributions
👉 Hierarchical Clustering

@quaesita 5/8
👉 Centroid-based Clustering organizes the data into non-hierarchical clusters
🔹 These algorithms are efficient but sensitive to initial conditions and outliers.
🔹 k-means is the most widely-used centroid-based clustering algorithm which is efficient, effective, & simple

@quaesita 6/8
👉 Density-based clustering connects areas of high example density into clusters.
🔹 Allows for arbitrary-shaped distributions as long as dense areas can be connected
🔹 These algorithms have difficulty with data of varying densities & high dimensions

@quaesita 7/8
👉 Distribution-based clustering assumes data is composed of distributions
🔹 such as Gaussian distributions
🔹 As distance from the distribution's center increases, the probability that a point belongs to the distribution decreases

#31DaysofML

@quaesita 8/8
👉 Hierarchical Clustering
🔹 Creates a tree of clusters
🔹 Well suited to hierarchical data, such as taxonomies
🔹 Advantage: Any number of clusters can be chosen by cutting the tree at the right level

#31DaysofML

Resource: bit.ly/gcp_ml