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Practical Data Science With Amazon SageMaker

at NetCom Learning

Course Details
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Live Online Training
Class Level: All levels
Age Requirements: 16 and older
Average Class Size: 5
System Requirements: Your Computer
  • Use a recent PC or Mac computer that is less than 4 years old.
  • Two display monitors are highly recommended
  1. one for joining the virtual classroom and, if applicable, viewing digital books
  2. the other for doing your labs
Your internet connection
  • Please use a wired, not wireless or wifi connection
  • Please use a broadband internet connection and not dial-up (modem)
Computer USB headsets (with headphone & microphone in one unit) that we recommend: 
  • Microsoft LifeChat LX-3000 or Logitech ClearChat H390.
Class Delivery: Live Online Training brings our award-winning classroom experience to you! Developed through rigorous testing of the leading virtual learning environments and multimedia technologies, Live Online Training - a.k.a. "LOT" - delivers the training industry's most complete remote classroom experience.

Features And Benefits Of Live Online Training Courses:

» Hands-on, live instructor-led training from any internet-accessible location
» Real-time remote access to class software and virtual lab environment
» Eliminate travel expenses and save the time spent in transit - Go Green
» LOT is compatible with PCs, Macs, and mobile devices

What Is Included In Your Live Online Training Tuition?
» NetCom's Award-Winning Training & Certification Prep
» Official Courseware and Lab Manuals for your class (PDF's - where applicable)
» Test-Prep Software if applicable

What you'll learn in this data science course:

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. 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.

Learning 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.



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

Who Should Go For This Training?

  • Developers

  • Data Scientists
Course Outline

    1. Introduction to machine learning
      1. Types of ML
      2. Job Roles in ML
      3. Steps in the ML pipeline
    2. Introduction to data prep and SageMaker
      1. Training and test dataset defined
      2. Introduction to SageMaker
      3. Demonstration: SageMaker console
      4. Demonstration: Launching a Jupyter notebook
    3. Problem formulation and dataset preparation
      1. Business challenge: Customer churn
      2. Review customer churn dataset
    4. Data analysis and visualization
      1. Demonstration: Loading and visualizing your dataset
      2. Exercise 1: Relating features to target variables
      3. Exercise 2: Relationships between attributes
      4. Demonstration: Cleaning the data
    5. Training and evaluating a model
      1. Types of algorithms
      2. XGBoost and SageMaker
      3. Demonstration: Training the data
      4. Exercise 3: Finishing the estimator definition
      5. Exercise 4: Setting hyper parameters
      6. Exercise 5: Deploying the model
      7. Demonstration: hyper parameter tuning with SageMaker
      8. Demonstration: Evaluating model performance
    6. Automatically tune a model
      1. Automatic hyper parameter tuning with SageMaker
      2. Exercises 6-9: Tuning jobs
    7. Deployment / production readiness
      1. Deploying a model to an endpoint
      2. A/B deployment for testing
      3. Auto Scaling
      4. Demonstration: Configure and test auto scaling
      5. Demonstration: Check hyper parameter tuning job
      6. Demonstration: AWS Auto Scaling
      7. Exercise 10-11: Set up AWS Auto Scaling
    8. Relative cost of errors
      1. Cost of various error types
      2. Demo: Binary classification cutoff
    9. Amazon SageMaker architecture and features
      1. Accessing Amazon SageMaker notebooks in a VPC
      2. Amazon SageMaker batch transforms
      3. Amazon SageMaker Ground Truth
      4. Amazon SageMaker Neo

Remote Learning

This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.

Upon registration, the instructor will send along additional information about how to log-on and participate in the class.

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School: NetCom Learning

NetCom Learning

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Since its inception in 1998, NetCom has trained over 95 percent of the Fortune 500, serviced over 23,000 business customers, and advanced the skills and careers of over...

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