Practical Data Science with Amazon SageMaker Course Details:

This one day course covers to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.

    Nov 25 2024

    Date: 11/25/2024 - 11/25/2024 (Monday - Monday) | 8:30 AM - 4:30 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Practical Data Science with Amazon SageMaker

    November 25, 2024 | 8:30 AM - 4:30 PM (EST) | Virtual Classroom Live


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    Dec 16 2024

    Date: 12/16/2024 - 12/16/2024 (Monday - Monday) | 8:30 AM - 4:30 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Practical Data Science with Amazon SageMaker

    December 16, 2024 | 8:30 AM - 4:30 PM (EST) | Virtual Classroom Live


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    Jan 17 2025

    Date: 01/17/2025 - 01/17/2025 (Friday - Friday) | 8:30 AM - 4:30 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Practical Data Science with Amazon SageMaker

    January 17, 2025 | 8:30 AM - 4:30 PM (EST) | Virtual Classroom Live


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This course covers the following concepts:

  • 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 relative cost of errors

*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.
  • 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.

We recommend that attendees of this course have working knowledge of a programming language.

A technical audience at an intermediate level

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