Implement AI/ML algorithm fundamentals

Create implementations for fundamental AI and machine learning algorithms:

## Basic ML algorithms:
- Linear regression
- Logistic regression
- k-Nearest Neighbors
- Decision trees
- Naive Bayes classifier
- k-Means clustering
- Principal Component Analysis

## Search algorithms:
- A* search
- Minimax with alpha-beta pruning
- Monte Carlo Tree Search
- Genetic algorithms
- Simulated annealing

## Neural network basics:
- Perceptron
- Simple feedforward network
- Backpropagation algorithm

## Requirements:
- Implement from scratch without using ML libraries
- Include detailed mathematical derivations
- Create visualization helpers
- Provide test cases with sample datasets

**Note:** The focus is on clear, educational implementations that demonstrate the core concepts, not on performance or production-readiness.

This collection will provide educational implementations of AI/ML algorithms to help understand their fundamental principles, separate from the many existing optimized libraries.