PySpark: Apply & Evaluate Predictive ML Models
$49
ENROLL NOWCourse Overview
What You'll Learn
- This intermediate-level course empowers learners to apply, analyze, and evaluate machine learning models using Apache PySpark’s distributed computing framework.
- 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.
- Module 2 builds on these foundations to cover multi-class classification using multinomial logistic regression and decision trees.
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.
Course FAQs
Is this an accredited online course?
Accreditation for 'PySpark: Apply & Evaluate Predictive ML Models' is determined by the provider, EDUCBA. For online college courses or degree programs, we strongly recommend you verify the accreditation status directly on the provider's website to ensure it meets your requirements.
Can this course be used for continuing education credits?
Many of the courses listed on our platform are suitable for professional continuing education. However, acceptance for credit varies by state and licensing board. Please confirm with your board and {course.provider} that this specific course qualifies.
How do I enroll in this online school program?
To enroll, click the 'ENROLL NOW' button on this page. You will be taken to the official page for 'PySpark: Apply & Evaluate Predictive ML Models' on the EDUCBA online class platform, where you can complete your registration.
