Machine Learning with Python: Case Studies

Overview

Learners completing this course will be able to apply regression, clustering, classification, and feature engineering techniques to real-world datasets, evaluate models with performance metrics, and visualize results for actionable insights. Through hands-on case studies, learners will not only understand algorithms but also gain the ability to prepare data, train models, and interpret outputs effectively. This course stands out by combining practical projects with step-by-step implementation using Python. Instead of focusing on theory alone, it demonstrates machine learning through applied case studies such as salary prediction, startup cost analysis, time series forecasting, face detection, fruit classification, and credit card default prediction. Learners benefit from structured progression—starting with foundational regression models, advancing through clustering and classification, and culminating in financial credit risk modeling with advanced evaluation techniques. By the end of the course, participants will confidently execute machine learning workflows in Python, analyze diverse datasets, and apply predictive models to solve real-world business and research problems. This unique emphasis on project-driven learning ensures that learners develop both technical expertise and problem-solving skills valued in today’s data-driven industries.

Syllabus

Foundations of Machine Learning Case Studies

This module introduces learners to machine learning projects through case studies, covering environment setup, regression methods, and logistic regression. By working with practical datasets, learners will build a strong foundation in modeling approaches and optimization techniques.

Clustering and Time Series Modeling

This module explores unsupervised learning with k-means clustering and introduces time series forecasting techniques. Learners gain hands-on practice with visualization, distance calculations, and analyzing sequential datasets such as airline passengers and Bitcoin prices.

Classification Algorithms in Practice

This module focuses on supervised learning techniques for classification. Learners apply algorithms such as logistic regression, decision trees, KNN, LDA, and Naive Bayes, while also visualizing decision boundaries to better interpret classifier behavior.

Credit Risk and Feature Engineering Projects

This module applies machine learning techniques to financial case studies, focusing on credit card default prediction. Learners practice data preparation, feature engineering, and evaluation using confusion matrices, AUC curves, and visualization with seaborn.