Build your Machine Learning Model and get accurate predictions without writing any Code using AWS SageMaker Canvas
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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.
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.
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.