| Description |
1 online resource (1 streaming video file (34 min., 53 sec.)) |
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data file |
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Videorecording |
| Note |
Title from title screen (Safari, viewed January 15, 2018). |
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Release date from resource description page (Safari, viewed January 15, 2018). |
| Performer |
Presenter, Mikhail Semeniuk. |
| Summary |
"Spark ML provides a rich set of tools and models for training, scoring, evaluating, and exporting machine learning models. This video walks you through each step in the process. You'll explore the basics of Spark's DataFrames, Transformer, Estimator, Pipeline, and Parameter, and how to utilize the Spark API to create model uniformity and comparability. You'll learn how to create meaningful models and labels from a raw dataset; train and score a variety of models; target price predictions; compare results using MAE, MSE, and other scores; and employ the SparkML evaluator to automate the parameter-tuning process using cross validation. To complete the lesson, you'll learn to export and serialize a Spark trained model as PMML (an industry standard for model serialization), so you can deploy in applications outside the Spark cluster environment."--Resource description page |
| Subject |
SPARK (Electronic resource)
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SPARK (Electronic resource) |
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Machine learning.
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Apprentissage automatique. |
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Machine learning. |
| Added Author |
Slepicka, Jason, author.
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| Added Title |
Hands-on guide to train, score, evaluate, and export machine learning models |
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