Supervised Machine Learning: Classification

This module introduces learners to logistic regression, a fundamental concept in supervised machine learning for classification. Topics covered include classification error metrics, implementing logistic regression models, and hands-on labs for practical application.

Explore K Nearest Neighbors as a classification technique, covering decision boundary, distance measurement, feature scaling, and practical applications through notebooks and lab exercises. Gain an in-depth understanding of the pros and cons of K Nearest Neighbors for classification.

Gain insight into Support Vector Machines, including classification, cost function, regularization, Gaussian kernels, and implementing kernel models. Utilize notebooks and lab exercises to deepen your understanding of Support Vector Machines for classification.

Delve into the world of decision trees, covering the overview of classifiers, entropy-based splitting, pros and cons, and practical application through notebooks and lab exercises. Gain expertise in building and analyzing decision trees for classification.

This module covers ensemble models, including bagging, random forest, boosting, stacking, and practical application through demo and practice labs. Gain hands-on experience in using ensemble methods for classification and understand their role in improving model performance.

Explore modeling unbalanced classes, including model interpretability, examples of self-interpretable and non-self-interpretable models, and various modeling approaches for handling unbalanced classes. Engage in practical exercises to deepen your understanding of modeling imbalanced classes in a data set.