Curriculum

Module 0: Introduction

What is Machine Learning
Types of ML: Supervised, Unsupervised, Reinforcement
Types of ML problems: Regression, Classification

Module 1: Linear Models

Linear Regression
Logistic Regression

Module 2: Model Evaluation

Training and Validation
Model Evaluation Metrics - Accuracy, RMSE, ROC, AUC, Confusion Matrix, Precision, Recall, F1 Score
Overfitting and Bias-Variance trade-off
Regularization (L1/L2)
K-fold Cross Validation

Module 3: Tree-based Models

Decision Trees
Bagging and Boosting
Random Forest
Gradient Boosting Machines
Feature Importance

Module 4: Model Selection

Model Pipelines
Feature Engineering
Ensemble Models (Advanced)
Unbalanced Classes (Advanced)