Unsupervised Learning

After this skill you can…

• Apply k-means and DBSCAN clustering
• Reduce dimensions with PCA and UMAP
• Use autoencoders for representation learning

Prerequisites

Watch (10 videos)

StatQuest: Random Forests Part 2: Missing data and clustering

StatQuest with Josh Starmer · beginner

→ Use a random forest model for clustering→ Interpret the results of a random forest model

K-Means Clustering Explained Simply 🤖

Analytics Vidhya · beginner hands-on

→ Implement K-Means clustering algorithm→ Choose the optimal number of clusters using the elbow method→ Handle outliers and non-spherical clusters

Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 17: Advancing Robot Intelligence

Stanford Online · beginner

→ Train models with reinforcement learning→ Apply reinforcement learning to robotics→ Use imitation learning for pre-training vision language models

K-Means - Explained

DataMListic · beginner hands-on

→ Apply K-Means Clustering to datasets→ Understand the importance of centroid initialization→ Identify clusters in unlabeled data

Machine Learning In 60 Seconds | Machine Learning Explained For Beginners | #Shorts | Simplilearn

Simplilearn · beginner

→ Apply unsupervised learning techniques→ Cluster data using unsupervised algorithms→ Visualize high-dimensional data

The Physics of Diffusion Models

Julia Turc · intermediate

→ Use diffusion models for generative tasks→ Sample new data from trained models

What are Neural Nework AutoEncoders ?

New Machina · beginner hands-on

→ Apply autoencoders to real-world problems→ Use autoencoders for dimensionality reduction and feature extraction

Machine Learning vs Deep Learning Explained in 10 Minutes 🧠 (Beginner Friendly!)

AI Study Hub · beginner hands-on

→ Identify patterns in data→ Cluster similar data points→ Reduce dimensionality

Stanford CS221 | Autumn 2025 | Lecture 13: Bayesian Networks and Gibbs Sampling

Stanford Online · beginner

→ Understand the limitations of rejection sampling and Gibbs sampling→ Optimize Gibbs sampling using the Markov blanket concept

Lecture 15 - PCA and ICA | Stanford CS229: Machine Learning Andrew Ng - Autumn 2018

Stanford Online · beginner

→ Apply clustering algorithms→ Use density estimation techniques→ Visualize high-dimensional data

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