1. Review of Machine Learning Fundamentals:

A brief overview of machine learning concepts, including supervised learning, unsupervised learning, and evaluation metrics.

2. Regression Algorithms:

2.1. Linear Regression:

Understanding linear regression.

Training linear regression models.

Evaluation metrics (e.g., Mean Absolute Error, Mean Squared Error, R-squared).

2.2. Polynomial Regression:

Extending linear regression to polynomial regression.

Overfitting and regularization.

2.3. Decision Trees and Random Forest for Regression:

Decision tree regression.

Ensemble methods like random forests for regression.

3. Classification Algorithms:

3.1. Logistic Regression:

Introduction to logistic regression for binary classification.

Multinomial logistic regression for multi-class classification.

3.2. Decision Trees and Random Forest for Classification:

Decision tree classification.

Ensemble methods like random forests for classification.

3.3. Support Vector Machines (SVM):

Understanding SVM for classification.

Kernel methods and non-linear separation.

3.4. k-Nearest Neighbors (k-NN):

Introduction to the k-NN algorithm.

Choosing the right value of k.

4. Model Evaluation Metrics:

4.1. Classification Metrics:

Accuracy, precision, recall, F1-score, and ROC-AUC.

Confusion matrices and their interpretation.

Handling imbalanced datasets.

4.2. Regression Metrics:

Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.

Interpreting regression evaluation metrics.

5. Cross-Validation:

K-fold cross-validation and its importance.

Cross-validation techniques for both regression and classification.

6. Hyperparameter Tuning:

Grid search and randomized search for finding optimal hyperparameters.

The concept of overfitting and underfitting during hyperparameter tuning.

7. Feature Selection and Importance:

Techniques for feature selection and dimensionality reduction.

Feature importance in tree-based models.

8. Model Interpretability (Brief Overview):

Basic techniques for interpreting and explaining machine learning models.

9. Real-world Projects and Hands-on Exercises:

- Practical exercises and projects involving regression and classification tasks.

- Application of different algorithms to real datasets.

10. Model Deployment (Brief Overview):

- Introduction to deploying machine learning models in production.

11. Ethical Considerations in Machine Learning:

- Addressing ethical concerns related to bias, fairness, and responsible AI in machine learning.