Practical Data Science with Amazon SageMaker

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Practical Data Science with Amazon SageMaker

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In this intermediate-level course, individuals learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use cases include customer retention analysis to inform customer loyalty programs.

Before taking this course, students should have:

  • Familiarity with Python programming language
  • Basic understanding of Machine Learning
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)

1 Day/Lecture & Lab

This course is designed for Developers and Data Scientists.

  • Introduction to Machine Learning
  • Introduction to Data Prep and SageMaker
  • Problem formulation and Dataset Preparation
  • Data Analysis and Visualization
  • Training and Evaluating a Model
  • Automatically Tune a Model
  • Deployment / Production Readiness
  • Relative Cost of Errors

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