Digital Classroom - Practical Data Science with Amazon SageMaker

סקירה כללית

Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, students will spend a day in the life of a data scientist so that students can collaborate efficiently with data scientists and build applications that integrate with ML.

Students will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. Students will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.

סילבוס

• 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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נושאים

Programming