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Practical Data Science with Amazon SageMaker

Prepping data sets for machine learning and thinking about results

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.

This course includes AWS Training Exclusives. Learn more!

GK# 100630 Vendor# Data Science

Vendor Credits:

Global Knowledge Delivered Course
Training Exclusives

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Who Should Attend?

A technical audience at an intermediate level

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.

Course Outline

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

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Prerequisites

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