AI & ML

Learn AI and machine learning through clear, practical guides that explain the core ideas before the math becomes overwhelming.

This section is designed for readers who want a beginner-friendly but technically honest path into machine learning. The focus is on intuition, simple examples, and a cluster structure that connects foundational models with related concepts.

Who this section is for

• beginners entering machine learning for the first time
• engineers who want to understand model logic before using tools
• readers who prefer connected learning paths over isolated tutorials

Start here

• Perceptron explained for beginners
• Neural networks basics
• What is an artificial neural network

Top guides

• Perceptron on the iris dataset
• Perceptron vs logistic regression
• Why perceptrons fail on xor
• Single-layer perceptron from scratch in python

Learning path

1. Start with the perceptron to understand weighted inputs, bias, and simple classification.
2. See the model on a real dataset such as Iris.
3. Understand where linear models fail, especially on non-linear problems such as XOR.
4. Move from single-layer intuition into neural networks and broader ML concepts.

Perceptron cluster

• Perceptron explained for beginners
• Perceptron on the iris dataset
• Perceptron vs logistic regression
• Why perceptrons fail on xor
• Single-layer perceptron from scratch in python

Read next

The best place to start is Perceptron explained for beginners. After that, continue to Neural networks basics to widen the picture.