Machine Learning Fundamentals

My journey into understanding machine learning from the ground up, focusing on both theoretical concepts and practical implementations.

Learning Objectives

• Understand supervised vs unsupervised learning
• Master fundamental algorithms (linear regression, decision trees, neural networks)
• Learn to evaluate and improve model performance
• Apply ML to real-world problems

Current Focus Areas

Mathematical Foundations

• Linear algebra essentials
• Statistics and probability
• Calculus for optimization

Core Algorithms

• Linear and logistic regression
• Decision trees and random forests
• Support vector machines
• Neural network basics

Practical Skills

• Data preprocessing and feature engineering
• Model evaluation metrics
• Cross-validation techniques
• Hyperparameter tuning