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ML-algorithms-implementation

This repository aims to provide clear and concise implementations of popular machine learning algorithms. The goal is to understand the underlying math and concepts behind machine learning models and practice building them from scratch.

Algorithms implemented

  1. Linear Regression

    • Multiple Linear Regression
    • Metrics (MAE, MSE, RMSE, MAPE, R2)
    • Regularization (L1, L2, ElasticNet)
    • Stochastic Gradient Descent

  2. Logistic Regression

    • Binary Logistic Regression
    • Metrics (Accuracy, Precision, Recall, F1, ROC AUC)
    • Regularization (L1, L2, ElasticNet)
    • Stochastic Gradient Descent