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Practical Data Science with Amazon SageMaker

Overview

This Amazon Web Services (AWS) intermediate-level course – Practical Data Science with Amazon SageMaker focuses on equipping participants with the skills and knowledge to solve a real-world use-case with machine learning and produce actionable results using Amazon SageMaker. The course covers the different stages of a typical data science process for machine learning from analyzing and visualizing a dataset to preparing the data and feature engineering. In this course, participants will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. The course also includes a customer retention analysis for building effective customer loyalty programs.

Schedule Classes

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 1 day

Live Virtual Classroom
Duration: 1 day

What You'll learn

  • Prepare a dataset for training
  • Train and evaluate a machine learning model
  • Automatically tune a machine learning model
  • Prepare a machine learning model for production
  • Think critically about machine learning model results

Outline

  • Business problem – Churn prediction
  • Load and display the dataset
  • Assess features and determine which Amazon SageMaker algorithm to use
  • Use Amazon SageMaker to train, evaluate, and automatically tune the model
  • Deploy the model
  • Assess the relative cost of errors
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Prerequisites

To attend this AWS course, participants need to be familiar with the Python programming language. They also need to have a basic understanding of machine learning.

Who Should Attend

The course is intended for a technical audience at an intermediate level, such as –

  • Developers
  • Data scientists

Interested in this course? Let’s connect!