What is ML? Types of ML

AI vs ML vs Deep Learning

Real-world ML applications

ML workflow understanding

NumPy for numerical operations

Pandas for data handling

Matplotlib & Seaborn for data visualization

Handling missing values

Outlier detection & treatment

Encoding categorical variables

Feature scaling (Normalization & Standardization)

Train-test split

Feature engineering

Linear Regression

Multiple Linear Regression

Polynomial Regression

Evaluation metrics (MAE, MSE, RMSE, R² Score)

Logistic Regression

K-Nearest Neighbors

Decision Trees

Random Forest

Naive Bayes

Support Vector Machine (SVM)

Evaluation metrics (Confusion Matrix, Accuracy, Precision, Recall, F1 Score)

K-Means

Hierarchical Clustering

DBSCAN