• Length:
    4 Weeks
  • Effort:
    2–4 hours per week
  • Price:

    Add a Verified Certificate for $99 USD

  • Institution
  • Subject:
  • Level:
  • Language:
  • Video Transcript:
  • Course Type:
    Self-paced on your time


About this course

Skip About this course

Machine learning is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market.

This course will teach you, an application developer, how to use Amazon SageMaker to simplify the integration of Machine Learning into your applications. Key topics include: an overview of Machine Learning and problems it can help solve, using a Jupyter Notebook to train a model based on SageMaker’s built-in algorithms and, using SageMaker to publish the validated model. You will finish the class by building a serverless application that integrates with the SageMaker published endpoint.

Learn from AWS Training and Certification expert instructors through lectures, demonstrations, discussions and hands-on exercises* as we explore this complex topic from the lens of the application developer.

*Note that there may be a cost associated with some exercises. If you do not wish to incur additional expenses, you may view demonstrations instead.

What you'll learn

Skip What you'll learn
  • Key problems that Machine Learning can address and ultimately help solve
  • How to train a model using Amazon SageMaker’s built-in algorithms and a Jupyter Notebook instance
  • How to publish a model using Amazon SageMaker
  • How to integrate the published SageMaker endpoint with an application

Welcome to Machine Learning with Amazon SageMaker

  • Course Introduction
    • Welcome to Machine Learning with SageMaker on AWS
    • Course Welcome and Student Information
    • Meet the Instructors
    • Introduce Yourself

Week 1

  • Introduction to Machine Learning with SageMaker on AWS
    • Introduction to Week 1
    • What we we use ML for?
    • Diving Right In
    • What is Amazon SageMaker
  • WeeklyQuiz, Readings, Resources, Discussion
    • Week 1 Notes and Resources
    • Week 1 Quiz
    • Week 1 Discussion

Week 2

  • Amazon SageMaker Notebooks and SDK
    • Introduction to Week 2
  • Amazon SageMaker Notebooks
    • Introduction to Jupyter Notebooks
    • Notebooks and Libraries: Cleaning and Preparing Data
    • Exercise 2.1 Walkthrough
    • Exercise 2.1: Create Your Notebook Instance (Optional)
  • Weekly Quiz, Readings, Resources, Discussion
    • Week 2 Notes and Resources
    • Week 2 Quiz
    • Week 2 Discussion

Week 3

  • Amazon SageMaker Algorithms
    • Introduction to Week 3
  • ML and Amazon SageMaker Terminology
    • SageMaker/ML Terminology and Algorithms
    • Hyperparameter Tuning
  • Amazon SageMaker Algorithms
    • k-means Algorithm Walkthrough
    • Introduction to Exercise 3.1
    • Exercise 3.1: Using the k-means Algorithm (Optional)
    • XGBoost Algorithm Walkthrough (Part 1)
    • XGBoost Algorithm Walkthrough (Part 2)
    • XGBoost Algorithm Walkthrough (Part 3)
    • Introduction to Exercise 3.2
    • Exercise 3.2: Using the XGBoost Algorithm (Optional)
  • Weekly Quiz, Readings, Resources, Discussion
    • Week 3 Notes and Resources
    • Week 3 Quiz
    • Week 3 Discussion

Week 4

  • Application Integration
    • Introduction to Week 4
  • Integrating Amazon SageMaker with your Applications
    • Serverless Recap
    • Exercise 4.1 Walkthrough
    • Exercise 4.1: Python Movie Recommender (Optional)
    • Bring Your Own Models
    • Bringing Your Own Models: MXNet and TensorFlow
  • Weekly Quiz, Readings, Resources, Discussion
    • Week 4 Notes and Resources
    • Week 4 Quiz
    • Class Wrap Up
    • Course Survey
    • Week 4 Discussion
  • End of Course Assessment (Verified Certificate Track Only)

Meet your instructors

Russell Sayers
Technical Architect - AWS T&C
Amazon Web Services
Asim Jalis
Senior Technical Trainer
Amazon Web Services
Carl Leonard
Technical Trainer
Amazon Web Services

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Frequently asked questions

Q. Are there any prerequisites for this course?
A. We recommend having at least one year of software development experience, and a basic understanding of AWS services and the AWS console, either through previous experience or the AWS Professional Developer Series on edX.

Q. Is it a requirement that I complete the AWS Professional Developer Series on edX before taking this course?
A. No this is not a requirement. However, this course assumes some understanding of several AWS services and the AWS console. If you do not have this experience, it may be beneficial for you to take at least one course from the AWS Professional Developer Series.

Q. Are there any costs associated with this course?
A. Learners can register for the course in an Audit track or Verified Certificate track. The Audit track is free, but has restrictions. The Verified Certificate track costs $99 and provides full access to course content, including graded assessments and assignments, for the duration. Please visit edx.org for more information.

In addition to course registration costs, this course provides optional hands-on exercises which will have an associated charge in your AWS account. The AWS Free Tier provides access to SageMaker for two months after account sign-up. Please familiarize yourself with Amazon SageMaker Pricing at aws.amazon.com/sagemaker/pricing/, and the AWS Free Tier at aws.amazon.com/free/.

Please note that the AWS Free Tier also has a limit on the amount of resources that you can consume before you begin accruing charges. If you perform these hands-on exercises, there is a chance you may incur charges on your AWS account. Please visit the AWS Free Tier page for more information.

Q. How much time will this course require?
A. If following the weekly schedule, learners should plan to spend 2-4 hours per week on this course. However, learners may complete the course at their own pace..

Q. Will I receive a certificate for this course?
A. Learners enrolled in the Verified Certificate path will receive a certificate upon successful completion of the course.

Q. What is the grading policy for this course?
A. All learners may take weekly quizzes, which are not graded and allow unlimited retries.

Learners in the Verified Certificate track are able to take the final course assessment in the course. The final assessment does not allow retries and requires a score of 65% or better to pass. Passing the final assessment is required to obtain the Verified Certificate.

Learners in the Audit track will not have access to the final assessment, and will not be able to earn a certificate.

Q. How are discussions used in this course?
A. This course has discussion groups aligned to each week of the course. We encourage learners to ask questions or offer suggestions and feedback. AWS Instructors will monitor the discussion groups to answer questions specific to the exercises and topics covered in the course.

Q. When will course content be available?
A. All course content will be available when the course opens on December 14, 2018. Since AWS frequently publishes service updates and new features/functionality, there may be a need to update the course content during its lifetime. If course content is updated, a notice will be placed on the course home page.

Q. Will this course help me prepare for an AWS Certification?
A. Earning an AWS Certification typically requires both knowledge and experience. While this course, if taken in isolation, will provide you with baseline information about Machine Learning and Amazon SageMaker, it likely will not equip you to earn an AWS Certification. For more information about AWS Certifications, including recommended training and experience requirements, visit aws.amazon.com/certification.

Q: Will my practice exam voucher code expire?
A: Yes, you will need to redeem your voucher code by August 31, 2020. If you try to redeem your voucher code after this date, the code will be invalid.