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Machine Learning with Sagemaker (AWS)
Valid through October 31, 2022
AWS Training Pass
Take advantage of flexible training options with the AWS Training Pass and get Authorized AWS Training for a full year.
OverviewThis Machine Learning with Sagemaker (AWS) course intended for data scientists and software engineers. The course combines an overview and understanding of Machine Learning concepts with specific implementation in SageMaker. In addition, it brings in other tools outside of SageMaker when required.
Machine Learning (ML) is the killer app for Big Data. Amazon Machine Learning brings the power of ML to a regular programmer and provides ML as a service. However, to use ML effectively, one needs to understand the models used and how to utilize them on Amazon. For each machine learning concept, we first discuss the foundations, its applicability and limitations. Then we explain the implementation and use, and specific use cases.
- Attain thorough understanding of popular machine learning algorithms, their applicability and limitations
- Practice the application of these methods in the Amazon machine learning environment
- Achieve clarity in the real-world use of machine learning by illustrating each method with practical use cases
- Data Scientists and Software Engineers
- Familiarity with programming in at least one language
- Be able to navigate Linux command line
- Basic knowledge of command line Linux editors (VI / nano)
- Basic familiarity with AWS (optionally may be provided in the first day on the course)
- Data ETL
- Go into one example in detail, implemented on AWS Redshift
- Provide pointer to other examples for self-study
- Machine learning
- Goals, results, supervised/unsupervised
- Which part of ML is implemented in the Amazon Machine Learning
- SageMaker (AWS) Overview
- Linear regression
- Logistic regression and multinomial logistic regression
- SVM, decision trees, random forests, neural networks
- Labs for every section above
- Other types of unsupervised learning
- Hierarchical clustering
- Mixture models
- Visualization examples for the models above
- Links to other visualizations for self-study
- SageMaker Details
- Using Built-in Algorithms
- Using Your Own Algorithms
- Using TensorFlow
- Using Apache MXNet
- Using Apache Spark
- Amazon SageMaker Libraries
- Authentication and Access Control
Self-Paced Training Info
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