PySpark: Apply & Evaluate Predictive ML Models

Overview

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.

Syllabus

Regression Techniques in PySpark

This module introduces learners to foundational and advanced regression modeling techniques using PySpark's MLlib. Learners begin with basic linear regression workflows including data preparation, feature assembly, and prediction. They then progress to more complex models such as Generalized Linear Regression and ensemble techniques like Random Forest Regression. The module culminates with logistic regression models designed for binary classification, enabling learners to construct and evaluate scalable machine learning pipelines for predictive analytics in distributed environments.

Classification and Clustering with PySpark

This module equips learners with the ability to build, train, and evaluate classification and clustering models using PySpark's machine learning library. It covers practical applications of multinomial logistic regression for multi-class problems, decision tree classifiers for rule-based predictions, ensemble methods like Random Forests for improved generalization, and unsupervised clustering techniques using the K-Means algorithm. Through hands-on demonstrations, learners gain proficiency in data preparation, model configuration, prediction interpretation, and model performance evaluation in large-scale distributed environments.