Digital Classroom - Practical Data Science with Amazon SageMaker

Overview

Dive into practical machine learning with Amazon SageMaker, mastering data preparation, model training, evaluation, and deployment through hands-on labs and real-world scenarios on AWS.

Syllabus

- Introduction to Data Science and AI/ML -- Overview of AI/ML in industry -- Role of data scientists -- Introduction to AWS and Amazon SageMaker - Data Preparation -- Understanding data requirements for ML -- Data collection and exploration -- Data cleaning and preprocessing techniques - Introduction to Amazon SageMaker -- Overview of SageMaker features and services -- Navigating the SageMaker interface -- Setting up the environment - Building ML Models -- Selecting and configuring an ML algorithm -- Feature engineering and selection -- Building a model with SageMaker - Training ML Models -- Training concepts and methodologies -- Configuring training jobs in SageMaker -- Monitoring and evaluating model training - Deploying ML Models -- Understanding endpoints and deployment options -- Model deployment with SageMaker -- Integration with applications and services - Collaborating with Data Scientists -- Workflow and communication best practices -- Case studies of AI/ML projects - Hands-on Labs and Demonstrations -- Lab: Data preparation and exploration -- Lab: Building and training models -- Lab: Deploying and testing models - Conclusion and Next Steps -- Recap of key learnings -- Resources for further learning -- Certification and career pathways in data science

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