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Practical Data Science with Amazon SageMaker

SS Course: 58033

Course Overview

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You will 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 case includes customer retention analysis to inform customer loyalty programs.                                                                  

Scheduled Classes

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10/25/24 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
12/19/24 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

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
  • Cost of various error types
  • Demo: Binary classification cutoff
  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

Prerequisites

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Familiarity with Python programming language
  • Basic understanding of Machine Learning

    Who Should Attend

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    Developers
    • Data Scientists

    Next Step Courses

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