This is an old revision of the document!
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.