Ameet Talwalkar

Ameet Talwalkar is an assistant professor of Computer Science at UCLA and a technical advisor for Databricks. His research addresses scalability and ease-of-use issues in the field of statistical machine learning, with applications in computational genomics. He led the initial development of the MLlib project in Apache Spark and is a co-author of the graduate-level textbook 'Foundations of Machine Learning' (2012, MIT Press). Prior to UCLA, he was an NSF post-doctoral fellow in the AMPLab at UC Berkeley. He obtained a B.S. from Yale University and a Ph.D. from the Courant Institute at NYU.
More info here.

Sort options

Distributed Machine Learning with Apache Spark (edX)

No sessions available
Distributed Machine Learning with Apache Spark (edX)
Course Auditing
EdX
University of California, Berkeley
BerkeleyX
Categories
CS: Software Engineering
Data Science
Statistics & Data Analysis
Effort
Intermediate
4 Weeks
5-10 Hours/Week
Languages
English
English
Distributed Machine Learning with Apache Spark (edX)
Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark. Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization.
Data Science and Engineering with Spark XSeries
Algorithms
Statistical Models
Model Evaluation
Supervised Learning
Exploratory Data Analysis
Spark
Machine Learning Pipelines
Apache Spark
Machine Learning
View more details
No sessions available
Course Auditing
EdX
University of California, Berkeley
BerkeleyX
CS: Software Engineering
Data Science
Statistics & Data Analysis
Intermediate
4 Weeks
5-10 Hours/Week
81.00 EUR
English
English