PySpark: Apply and Evaluate Predictive ML Models

This intermediate-level course empowers learners to apply, analyze, and evaluate machine learning models using Apache PySpark’s distributed computing framework. Designed for data professionals familiar with Python and basic ML concepts, the course explores real-world implementation of both regression and classification techniques, along with unsupervised clustering. In Module 1, learners will construct linear and generalized regression models, apply ensemble regressors such as Random Forests, and evaluate predictive performance using metrics like RMSE and R-squared. The module concludes with an in-depth look at logistic regression for binary classification tasks. Module 2 builds on these foundations to cover multi-class classification using multinomial logistic regression and decision trees. Learners will also evaluate ensemble models like Random Forests for robust classification, and explore K-Means clustering for unsupervised learning problems. Each concept is reinforced with guided PySpark code demonstrations, predictive workflows, and practical evaluations using large datasets. By the end of the course, learners will be able to design, execute, and critically assess machine learning models in PySpark for scalable data analytics solutions

• Build and evaluate regression models using PySpark
• Apply logistic regression, decision trees, and Random Forests for classification
• Implement K-Means clustering and assess distributed ML workflows

• Basic Python knowledge and ML concepts
• General computer and internet skills