Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

PySpark: Apply & Evaluate Predictive ML Models

EDUCBA via Coursera

Go to class Write review

Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Unlock All Certificates

Take your PySpark machine learning skills to the next level by learning how to apply and evaluate predictive models for scalable data analytics. This intermediate-level course is designed for learners with Python knowledge and a foundation in machine learning who want to build, assess, and interpret machine learning models using Apache PySpark and MLlib. You will begin by constructing linear regression models before progressing to Generalized Linear Regression, Random Forest Regression, and logistic regression for binary classification. Next, you will explore multinomial logistic regression, decision tree classifiers, Random Forest classification, and K-Means clustering for unsupervised learning. Throughout the course, you will reinforce each concept with guided PySpark code demonstrations, predictive workflows, model evaluation techniques, and practical analysis using large datasets. By the end of the course, you will be able to design, execute, and evaluate regression, classification, and clustering models in PySpark while interpreting model performance using appropriate evaluation methods. If you are looking to strengthen your ability to build scalable machine learning workflows in distributed environments, this course provides practical experience with widely used predictive modeling techniques in PySpark.

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.