Notebook Gallery

Explore practical implementations of the machine learning concepts covered in this site. Each notebook includes hands-on examples, visualizations, and explanations to reinforce the theory.

Supervised Learning

• Linear Regression
  📄 View on GitHub
  

• Logistic Regression
  📄 View on GitHub
  

• Gaussian Discriminant Analysis
  📄 View on GitHub
  

• K-Nearest Neighbors (KNN)
  📄 View on GitHub
  

• Perceptron Algorithm
  📄 View on GitHub
  

• Neural Networks
  📄 View on GitHub
  

Unsupervised Learning

• K-Means Clustering
  📄 View on GitHub
  

• Principal Component Analysis (PCA)
  📄 View on GitHub
  

Supporting Concepts

• Activation Functions
  📄 View on GitHub
  

Tip: Click the “Open in Colab” badge to launch and interact with each notebook directly in your browser — no setup required! After opening a notebook in Colab, make sure to run:

!pip install ek_ml_package

This installs the necessary package to enable all examples and code to run correctly.