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Machine Learning with Sagemaker (AWS)

Course Code

BD83

Duration

3 Days

Participants should be:

  • Familiar with programming in at least one language
  • Able to navigate Linux command line
  • Understand basic knowledge of command line Linux editors (VI / nano)
  • Understand basic familiarity with AWS (optionally may be provided in the first day on the course)
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.

This course is intended for data scientists and software engineers. It maintains an optimal balance of theory and practice. 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. This is achieved through a combination of about 50% lecture, 50% lab work.

Amazon SageMaker is a fully managed machine learning service. The course combines overview and understanding of Machine Learning concepts with specific implementation in SageMaker. In addition, it brings in other tools outside of SageMaker when required.
This course is designed for Data Scientists and Software Engineers.

In this course, participants will:

  • 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

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

Supervised Learning
Linear regression
Logistic regression and multinomial logistic regression
SVM, decision trees, random forests, neural networks
Labs for every section above

Unsupervised learning
K-Means
Other types of unsupervised learning

  • Hierarchical clustering
  • Mixture models
  • DBSCAN

Data visualization
Visualization examples for the models above
Links to other visualizations for self-study

SageMaker
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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