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    Practical Data Science with SageMaker - 1 Day Instructor-Led Training

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    In this intermediate-level course, you will learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker.
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    Practical Data Science with SageMaker - 1 Day Instructor-Led Training

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    Overview

    Course Overview

    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.

    Start your AWS Machine Learning journey by accessing Official AWS e-Learning for FREE. Learn What is Machine Learning, AWS Foundations: Machine Learning Basics, The Machine Learning Process and more - GET STARTED 

    Level: Intermediate

    Duration: 1 Day

    Delivery Type: Instructor-Led Training

    Course Objectives

    • 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

    Prerequisites

    Required

    • Familiarity with Python programming language
    • Basic understanding of Machine Learning

    Who Should Go For This Training?

    • Developers
    • Data Scientists

    Course Outline

    Day 1

    Module 1: Introduction to machine learning

    • Types of ML
    • Job Roles in ML
    • Steps in the ML pipeline

    Module 2: Introduction to data prep and SageMaker

    • Training and test dataset defined
    • Introduction to SageMaker
    • Demonstration: SageMaker console
    • Demonstration: Launching a Jupyter notebook

    Module 3: Problem formulation and dataset preparation

    • Business challenge: Customer churn
    • Review customer churn dataset

    Module 4: Data analysis and visualization

    • Demonstration: Loading and visualizing your dataset
    • Exercise 1: Relating features to target variables
    • Exercise 2: Relationships between attributes
    • Demonstration: Cleaning the data

    Module 5: Training and evaluating a model

    • Types of algorithms
    • XGBoost and SageMaker
    • Demonstration: Training the data
    • Exercise 3: Finishing the estimator definition
    • Exercise 4: Setting hyper parameters
    • Exercise 5: Deploying the model
    • Demonstration: hyper parameter tuning with SageMaker
    • Demonstration: Evaluating model performance

    Module 6: Automatically tune a model

    • Automatic hyper parameter tuning with SageMaker
    • Exercises 6-9: Tuning jobs

    Module 7: Deployment / production readiness

    • Deploying a model to an endpoint
    • A/B deployment for testing
    • Auto Scaling
    • Demonstration: Configure and test auto scaling
    • Demonstration: Check hyper parameter tuning job
    • Demonstration: AWS Auto Scaling
    • Exercise 10-11: Set up AWS Auto Scaling

    Module 8: Relative cost of errors

    • Cost of various error types
    • Demo: Binary classification cutoff

    Module 9: Amazon SageMaker architecture and features

    • Accessing Amazon SageMaker notebooks in a VPC
    • Amazon SageMaker batch transforms
    • Amazon SageMaker Ground Truth
    • Amazon SageMaker Neo

    Highlights

    • This training also includes, real life use case includes customer retention analysis to inform customer loyalty programs.

    Details

    Delivery method

    Pricing

    Custom pricing options

    Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.

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    Support

    Vendor support

    To learn more about our AWS trainings please visit NetCom Learning  or do not hesitate to contact our Sales Team: aws@netcomlearning.com  | (888)563-8266