Scalable Machine Learning
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This course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including feature extraction, supervised learning, model evaluation, and exploratory data analysis. Students will gain hands-on experience applying these principles by using Apache Spark to implement several scalable learning pipelines.
Scalable Machine Learning
- 1. Assistant Professor of Computer Science University of California, Los Angeles Visiting Assistant Professor Department of Electrical Engineering and Computer Science University of California, Berkeley Technical Advisor Databricks Ameet Talwalkar HONOR CODE CERTIFICATE Verify the authenticity of this certificate at Berkeley CERTIFICATE HONOR CODE Vassilios Rendoumis successfully completed and received a passing grade in CS190.1x: Scalable Machine Learning a course of study offered by BerkeleyX, an online learning initiative of The University of California, Berkeley through edX. Issued August 06, 2015 https://verify.edx.org/cert/be0873ea0e83411799973035e8f5d99a