Supervised ML - Regression and Classification

• Define machine learning
• Define supervised learning
• Define unsupervised learning
• Write and run Python code in Jupyter Notebooks
• Define a regression model
• Implement and visualize a cost function
• Implement gradient descent

• Optimize a regression model using gradient descent

• Use vectorization to implement multiple linear regression

• Use feature scaling, feature engineering, and polynomial regression to improve model training

• Implement linear regression in code

• Use logistic regression for binary classification

• Implement logistic regression for binary classification
• Address overfitting using regularization, to improve model performance