- From www.edx.org
Advanced Distributed Machine Learning with Spark
- 4 Sequences
- Introductive Level
- Starts on September 14, 2016
- Ends on September 5, 2016
Course details
Syllabus
- Training and deploying large-scale learning pipelines for various supervised and unsupervised settings
- Model parallelism and tradeoffs between computation and communication in distributed settings
- Collaborative filtering, decision trees, random forests, clustering, topic modeling, hyperparameter tuning
- Application of these principles using Spark, focusing on the spark.ml package
Prerequisite
Instructors
- Ameet Talwalkar
- Jon Bates
Editor
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