Classification and Hypothesis Testing in Machine Learning
Course Summary
This course provides a comprehensive understanding of classification algorithms and hypothesis testing for data-driven decision-making. Participants will explore supervised classification techniques, including logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks, alongside statistical hypothesis testing methods to assess model significance and reliability. The course integrates real-world applications, such as medical diagnosis, fraud detection, sentiment analysis, and scientific research, using Python and Scikit-learn.
Key Learning Outcomes
- Understand classification principles and their role in predictive modeling.
- Implement logistic regression, decision trees, SVM, and ensemble classifiers.
- Evaluate model performance using confusion matrices, precision-recall, F1-score, and ROC-AUC curves.
- Apply hypothesis testing techniques (t-tests, chi-square tests, ANOVA) to validate predictions and statistical insights.
- Work on real-world classification tasks, including spam detection, sentiment analysis, and medical diagnostics, etc.
