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Machine Learning with PySpark
This course is part of PySpark for Data Science.
Course Cost
Free course
Intermediate
Skill Level
11 Hours
Self-paced lessons
Machine Learning with PySpark introduces the power of distributed computing for machine learning, equipping learners with the skills to build scalable machine learning models. Through hands-on projects, you will learn how to use PySpark for data processing, model building, and evaluating machine learning algorithms. The course covers both supervised and unsupervised learning techniques, including linear regression, logistic regression, decision trees, and clustering methods like K-means. You'll also explore association rule mining and learn how to evaluate model performance using various metrics. By the end of this course, you'll understand PySpark's architecture, be able to load and process large-scale datasets, build and optimize machine learning models with PySpark's MLlib, and apply these skills to real-world case studies in different industries. The course strikes a balance between theoretical knowledge and practical application, making it perfect for data professionals who want to leverage distributed computing for machine learning tasks. You'll gain hands-on experience through numerous assignments and projects, culminating in a comprehensive house price prediction project.
What you'll learn
Implement machine learning models using PySpark MLlib
Build and optimize classification and regression models for predictive analysis
Apply clustering methods to group unlabeled data using K-means
Master association rule mining techniques for pattern discovery
Evaluate model performance using various metrics like RMSE and R-squared
Process and manipulate large-scale datasets with PySpark's DataFrame API
Apply machine learning to real-world applications across multiple industries
Implement a complete house price prediction model
Skills you'll gain
This course includes:
6.2 Hours PreRecorded video
14 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course on Machine Learning with PySpark equips learners with the skills to implement scalable machine learning models using Apache Spark's Python API. The curriculum begins with an introduction to PySpark MLlib and machine learning fundamentals before delving into both supervised and unsupervised learning techniques. Students learn to implement linear and logistic regression models, decision trees, K-means clustering, and association rule mining with FP-Growth. The course emphasizes practical applications through industry-specific case studies, including customer churn prediction, market basket analysis, and predictive maintenance. Throughout the modules, students gain hands-on experience with data processing, model building, evaluation metrics, and optimization techniques in a distributed computing environment. The program culminates with a project on house price prediction, allowing learners to apply their newfound skills to a real-world scenario.
Introduction to PySpark Machine Learning
Module 1 · 4 Hours to complete
Advanced PySpark Machine Learning
Module 2 · 4 Hours to complete
Applications and Case-Studies
Module 3 · 2 Hours to complete
Course Wrap-Up and Assessment
Module 4 · 1 Hours to complete
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Faculties
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