🧠 Machine Learning Fundamentals

Over the summer, I studied the fundamentals of machine learning for both regression and classification, applying concepts through interactive exercises and programming projects.

Regression

Linear regression
Loss functions
Model parameters
Gradient descent
Hyperparameters
Logistic regression with probability calculation, loss, and regularization

Classification

Thresholds and decision boundaries
Confusion matrix
Accuracy, precision, recall
ROC/AUC
Prediction bias
Multi-class classification

I applied these concepts through hands-on programming exercises and projects, building a foundation to explore more advanced machine learning techniques and real-world applications.