The AI Neurons
Machine Learning
Algorithms, statistics, and the foundations of intelligent systems.
Decoding Machine Learning
From "Magic" to Mathematics. Understanding the shift from traditional programming to data-driven learning.
Linear Classifiers
The hidden engine of AI. Understanding how machines draw lines to separate classes.
The Perceptron
Learning by making mistakes. The foundational algorithm that learns through trial and error.
Support Vector Machines
Finding the widest street. Margin maximization and the mathematics of robust classification.
Feature Representation
Turning raw data into learnable signals. From One-Hot encoding to modern Embeddings.
Regression
Predicting the Continuous World. From the Line of Best Fit to Loss Functions and Gradient Descent.
Gradient Descent
The Compass of Learning. How algorithms navigate the landscape of errors to find optimal solutions.