Build your Machine Learning Model and get accurate predictions without writing any Code using AWS SageMaker Canvas
InstructorPrince PatniComidoc has tracked 10 coupons for this course since 2025, last checked 2 months, 20 days ago. On average, a new coupon appears roughly every 43 days.
| Coupon code | Discount | Added | Status | Lifetime |
|---|---|---|---|---|
| Coupon code2026_AUG | Discount100% off | Added | StatusExpired | Lifetime~25 daysRan full term |
| Coupon codeAUGUST_2026 | Discount100% off | Added | StatusExpired | Lifetime30d 15h |
| Coupon codeJULY_2026 | Discount100% off | Added | StatusExpired | Lifetime30d 6h |
| Coupon codeFIFA_WORLDCUP_2026 | Discount100% off | Added | StatusExpired | Lifetime~30 daysRan full term |
| Coupon code2026_JUNE | Discount100% off | Added | StatusExpired | Lifetime~25 daysRan full term |
| Coupon codeMAY_2026 | Discount100% off | Added | StatusExpired | Lifetime~30 daysRan full term |
Lifetime is the time from a coupon's first appearance to its expiration or invalidation.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines three SageMaker Canvas use cases: predicting customer churn using consumption data, improving delivery times via supply chain optimization, and maximizing revenue through dynamic pricing.
This slide outlines the core advantages of SageMaker Canvas, highlighting its ability to generate predictions without coding, prepare data, utilize AutoML, and validate models.
This slide outlines the prerequisites for the tutorial, stating that only a basic awareness of machine learning is needed while no coding or modeling expertise is required.
The training follows a sequence of practical use cases, including Banknote Authentication, Spam SMS Detection, Customer Churn Prediction, and Wine Quality Prediction.
Instruction covers essential AWS setup, including configuring SageMaker Domains and preparing data within S3 Buckets.
Learner signals indicate a lack of detail regarding specific concepts like customer churn and how to build datasets independently.
The curriculum aligns with the goal of no-code machine learning for various backgrounds.