This repository is a self-directed machine learning curriculum inspired by Siraj Raval’s 100 Days of ML Code challenge. It provides daily lessons covering core ML concepts including data preprocessing, linear regression, multiple linear regression, logistic regression, support vector machines, and naive Bayes classification. Each day includes executable code in Python using scikit-learn and explanatory infographics. The repository serves as both a learning guide and practical implementation reference for beginners building ML fundamentals.