01-introduction

03-Methodologies Regression, Classification

04-Artificial intelligence Logistic , Polynomial Regression

05-metrics, Accuracy, precision, recall, F1 Score, ROC, AUC ,MSE, RMSE, MAE, R2 Score

06-Underfitting and Overfitting in Machine Learning

07-Artificial Neural Networks ANN & Generative Adversarial Networks GANs

08-Ethics & data bias