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.