CS125x: Advanced Distributed Machine Learning with Apache Spark
Offered By: University of California, Berkeley via edX
Course Description
Overview
Building on the core ideas presented in Distributed Machine Learning with Spark, this course covers advanced topics for training and deploying large-scale learning pipelines. You will study state-of-the-art distributed algorithms for collaborative filtering, ensemble methods (e.g., random forests), clustering and topic modeling, with a focus on model parallelism and the crucial tradeoffs between computation and communication.
After completing this course, you will have a thorough understanding of the statistical and algorithmic principles required to develop and deploy distributed machine learning pipelines. You will further have the expertise to write efficient and scalable code in Spark, using MLlib and the spark.ml package in particular.
After completing this course, you will have a thorough understanding of the statistical and algorithmic principles required to develop and deploy distributed machine learning pipelines. You will further have the expertise to write efficient and scalable code in Spark, using MLlib and the spark.ml package in particular.
Taught by
Ameet Talwalkar and Jon Bates
Tags
Related Courses
Business Considerations for 5G with Edge, IoT, and AILinux Foundation via edX FinTech for Finance and Business Leaders
ACCA via edX AI-900: Microsoft Certified Azure AI Fundamentals
A Cloud Guru AWS Certified Machine Learning - Specialty (LA)
A Cloud Guru Azure AI Components and Services
A Cloud Guru