Module 1: Machine Learning Fundamentals

1. Introduction to AI & ML
   • AI vs Machine Learning vs Deep Learning
   • Types of Machine Learning (Supervised, Unsupervised)
2. Supervised Learning
   • Regression Models:

o Linear Regression

o Polynomial Regression

o Ridge Regression

o Lasso Regression

• Classification Models:

o Logistic Regression

o K-Nearest Neighbors (KNN)

o Support Vector Machine (SVM)

o Decision Trees

o Random Forest

o XGBoost

o CatBoost

3. Unsupervised Learning
   • Clustering:

o K-Means

o DBSCAN

o Hierarchical Clustering

• Dimensionality Reduction:

o Principal Component Analysis (PCA)

o t-SNE

o LDA

4. Model Evaluation

• Train-Test Split
• Cross-validation
• Bias-Variance Tradeoff
• Evaluation Metrics:

o Accuracy

o Precision

o Recall

o F1-Score

o ROC-AUC

5.  Tools & Implementation
   • NumPy (numerical operations)
   • Pandas (dataset handling – basic usage)
   • Scikit-learn (model building)
   • Data preprocessing:

• Encoding (Label, One-hot)

Feature scaling