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Practical Data Science with Amazon SageMaker

SS Course: 58033

Course Overview

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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.                                                                  

Scheduled Classes

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09/02/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led
10/07/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led
10/14/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led
11/18/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led
12/02/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led

What You'll Learn

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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

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Viewing outline for:
Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline
  • Training and Test dataset defined
  • Introduction to SageMaker
  • Demo: SageMaker console
  • Demo: Launching a Jupyter notebook
  • Business Challenge: Customer churn
  • Review Customer churn dataset
  • Demo: Loading and Visualizing your dataset
  • Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demo: Cleaning the data
  • Types of Algorithms
  • XGBoost and SageMaker
  • Demo 5: Training the data
  • Exercise 3: Finishing the Estimator definition
  • Exercise 4: Setting hyperparameters
  • Exercise 5: Deploying the model
  • Demo: Hyperparameter tuning with SageMaker
  • Demo: Evaluating Model Performance
  • Automatic hyperparameter tuning with SageMaker
  • Exercises 6-9: Tuning Jobs
  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling Scaling
  • Demo: Configure and Test Autoscaling
  • Demo: Check Hyperparameter tuning job
  • Demo: AWS Autoscaling
  • Exercise 10-11: Set up AWS Autoscaling

Prerequisites

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Familiarity with Python programming language

    Who Should Attend

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    Developers
    • Data Scientists

    Next Step Courses

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