Machine Learning with Python

ML foundations: features, target, train/test split, validation, bias/variance, overfitting, and underfitting.
Regression: Linear, Multiple Linear, Polynomial, Ridge, and Lasso.
Classification: Logistic Regression, KNN, Decision Trees, Random Forest, and Naive Bayes.
Unsupervised learning: K-Means, hierarchical clustering, and PCA.
Feature engineering: scaling, encoding, transformations, and selection.
Evaluation: MAE, MSE, RMSE, R▓, accuracy, precision, recall, F1, confusion matrix, and ROC-AUC.

Capstone projects: Customer Churn Prediction; Insurance Claims Risk Analysis.