Learners will be able to explain core AI concepts, differentiate machine learning techniques, analyze AWS AI services, apply model training workflows, and evaluate end-to-end AI solutions using real-world examples. This course equips participants with a complete understanding of Artificial Intelligence fundamentals while developing practical cloud skills using AWS tools such as SageMaker, Comprehend, Rekognition, Lex, and Polly.

Master AI & AWS Cloud Skills: Analyze, Build, Deploy

Master AI & AWS Cloud Skills: Analyze, Build, Deploy
This course is part of AI Technologies, Tools & Cloud Certification Specialization Specialization

Instructor: EDUCBA
Access provided by ExxonMobil
Recommended experience
What you'll learn
Explain core AI and machine learning concepts and differentiate common ML techniques.
Use AWS AI services to build, train, and deploy intelligent applications.
Design end-to-end AI solutions and prepare for the AWS Certified AI Practitioner exam.
Skills you'll gain
- Reinforcement Learning
- Data Preprocessing
- Machine Learning
- Deep Learning
- AWS SageMaker
- Amazon Web Services
- Model Evaluation
- Unsupervised Learning
- Computer Vision
- Responsible AI
- Supervised Learning
- Natural Language Processing
- Prompt Engineering
- Model Deployment
- Image Analysis
- Artificial Intelligence
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16 assignments
January 2026
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There are 4 modules in this course
This module introduces the fundamental principles of Artificial Intelligence and Machine Learning, covering key concepts such as NLP, Computer Vision, learning paradigms, and essential analytical techniques. Learners develop a strong conceptual foundation to support deeper exploration of AI applications and AWS-based ML workflows.
What's included
9 videos4 assignments
This module explores the AWS AI ecosystem, introducing essential cloud-based AI services such as SageMaker, Comprehend, DeepLens, and foundational implementation patterns. Learners gain hands-on understanding of how AWS accelerates AI development through managed services, automation, and real-world case studies.
What's included
9 videos4 assignments
This module focuses on building conversational, vision-based, and multi-service AI solutions using AWS. Learners gain experience integrating Lex, Polly, Rekognition, and other services to design intelligent, end-to-end cloud applications while exploring foundation models and model engineering best practices.
What's included
7 videos4 assignments
This module covers the complete ML lifecycle, including model training, optimization, evaluation, deployment, ethical AI practices, prompt engineering, and continuous improvement strategies. Learners also prepare for certification through exam-focused insights and advanced scenario-based analysis.
What's included
8 videos4 assignments
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