AWS SageMaker: Machine learning made easy

Learn the ML platform that Fortune 500 companies want you to know with this new course

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1 month

Average completion time

$184 discount

through SDS Club

$ 15 $199
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Learn from Industry Leaders

Your instructor Dr. Ryan Ahmed has held engineering positions at Fortune 500 companies, such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He is a Stanford-certified Project Manager (SCPM), certified Professional Engineer (P.Eng.), a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). Ryan is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC ’17) in Chicago, USA.




five-star reviews


rating in 49 courses

Industry-leading tool

Machine learning is the hottest field to master in tech! Fields as diverse as banking, healthcare and transportation use it for their daily operations, from predicting sales to optimising work processes.

Hands-on learning

Get to grips with data engineering and feature engineering. Learn how to build and train the right model for your AI or machine learning project. Fine-tune hyperparameters and take control of the learning process. Deploy all these tasks in SageMaker, and see your work in action!

Learn using real cases

SuperDataScience students want to hit the ground running. Tinker with Kaggle datasets and build ML models for six real-world scenarios, from predicting medical insurance to recognizing traffic signs.

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  • Train and deploy AI/ML models using AWS SageMaker
  • Optimize model parameters using hyperparameters optimization search.
  • Develop, train, test and deploy linear regression model to make predictions.
  • Develop a deploy deep learning-based model to perform image classification.
  • Deploy production level multi-polynomial regression model to predict store sales based on the given features.
  • Develop time series forecasting models to predict future product prices using DeepAR.
  • Develop and deploy sentiment analysis model using SageMaker.

Why Should I Take This Course?

Industry-leading tool

Fortune 500 users


SageMaker belongs to Amazon Web Services (AWS), which are one of the most widely used cloud computing platforms in the world.

Major companies like Netflix, LinkedIn and Adobe use Amazon Web Services (AWS). Amazon SageMaker was introduced to the AWS suite in 2017—and businesses across industries took notice:

Volkswagen Group now uses SageMaker for its manufacturing plants.
JPMorgan Chase uses SageMaker for its AI platform.

These are just two of many more Fortune 500 companies that are using SageMaker to get ahead.

50+ slides

Real-world case studies

Expert instructor

There is a huge amount to cover in AWS SageMaker. Its capabilities for machine learning are nearly limitless. You name it, and SageMaker will very likely let you do it.

Become a Top AWS SageMaker Specialist

Career Karma declared “Machine learning engineer” the best job of 2020, due to growing demand. report average salaries of $146,085. 


Not in it for the money? Learning ML doesn’t only make sense from a financial perspective: the industry is growing by 344%, so learning ML is a great way to future-proof your career.

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Average salary

for an AWS SageMarker Specialist in the U.S.

What You Will Get

Expert Instructors

  • Learn from the Industry Leaders
  • 1M+ students
  • 50,000 five-star reviews
  • 49 Courses Rated 4.5+

On-Demand Lifetime Content

  • Self-paced course
  • On-demand video lectures
  • Lifetime access
  • Mobile and TV viewing

Intensive Content

  • 8.5 hours of video lectures
  • 9 articles
  • 5 downloadable resources
  • 3 valuable bonuses

Career Credentials

  • Earn a certificate of completion
  • Work with real-world scenarios
  • Learn the math behind neural networks

People Talk About Us

Comprehensive course content

AWS is the one of the most widely used cloud computing platforms in the world.

AWS SageMaker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently, and it's a technology that's not only hot in the market right now, but one that should be in your toolbelt as well!

See eye to eye with the top SageMaker specialists by creating production-level AI/ML models, as well as having in-depth knowledge on linear learner, XGBoost, deepAR, image classification, and PCA.

If that's not enough to convince you, you'll also learn how to spin hyperparameters optimization job, create and invoke endpoints, and improve model performance.

Take a dive into the latest technologies

One of the unique features covered in the course is the use of the brand new sagemaker studio.

SageMaker Studio offers a web-based interface where all machine learning development stages such as model building, training, and deployment could be perfumed in one place resulting in an enhanced productivity.

SageMaker studio allows developers to manage "experiments" by automatically keeping track of model parameters, inputs and model artifacts in one place.

A million students have already chosen SuperDataScience

It’s time for you to Join the Club!