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

סקירה כללית

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.

Through clear video-based instruction and structured module progression, learners gain the ability to design conversational agents, build computer vision pipelines, prepare datasets, train and optimize ML models, implement foundation models, and deploy intelligent cloud applications. By completing hands-on demonstrations and graded assessments, learners strengthen their technical confidence and prepare for the AWS Certified AI Practitioner exam.

What makes this course unique is its step-by-step learning path, AWS-focused practical guidance, and real-world case studies that connect AI theory with cloud implementation. The course blends foundational knowledge with applied AWS workflows, making it ideal for beginners, aspiring cloud practitioners, and professionals seeking to expand their AI and ML capabilities.

סילבוס

• Foundations of AI & Machine Learning
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.
• AWS AI & ML Services Deep Dive
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.
• Building Intelligent Solutions with AWS
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.
• Model Lifecycle, Governance & Exam Prep
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.

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נלמד על ידי

EDUCBA

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נושאים

Programming