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Machine Learning with Sagemaker (AWS)
Duration
3 Days
Course
DCSK-125
Available Formats
Classroom Training, Online Training
Course Description
Overview
This 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.
Objectives
Upon completion of the Machine Learning with Sagemaker (AWS) course, students will be able to:
- 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
Audience
- Data Scientists and Software Engineers
Prerequisites
- 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)
Topics
Introductions and Overviews
- 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
- K-Means
- Other types of unsupervised learning
- Hierarchical clustering
- Mixture models
- DBSCAN
- Visualization examples for the models above
- Links to other visualizations for self-study
- Intro
- 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
- Monitoring
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Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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title
Please take a moment to fill out this form. We will get back to you as soon as possible.
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