AWS Certified AI Practitioner (AIF-C01)

Level: Beginner, 12 Sessions (48 hours) – Tuition details will be shared during the free info session

  • 100% coverage of the AWS AI Practitioner Exam Topics with hands-on experience
  • In-class internship opportunity, with industry relevant capstone project
  • Presentation skills (soft skills)
  • Interview preparations and resume workshops
  • Taught by former AWS Sr. Technical Account Manager & Cisco Academy Instructor

This course will enable you to demonstrate:

  • Understanding of the fundamental concepts and services related to AI and ML, including their applications in real-world scenarios.
  • Discovering the steps to build, train, and deploy machine learning models using AWS tools and services.
  • Exploring Responsible AI practices for developing fair, transparent, and explainable AI solutions.
  • Discussing the best practices for securing AI/ML workloads and ensuring compliance with AWS security standards.

Details of AWS Certified AI Practitioner Exam

  • Career Advancement: This certification validates that you understand the basics of artificial intelligence. It also makes you a valuable asset in the AI-driven market of today. It leads to a number of positions, such as ML engineers, data scientists, and AI consultants.
  • Increased Earning Potential: Possessing this certification place you in a better position to demand greater pay and benefits packages.
  • Development of Skills: The AWS AI Practitioner (AIF-C01) covers a broad range of ML and AI concepts. Generative AI, computer vision, deep learning, machine learning algorithms, and natural language processing are part of the learnings
  • Strategic Business Advantage: By comprehending AI principles, you can find opportunities and work with AI teams more successfully. Make well-informed decisions about AI adoption and implementation.

Become AI Skilled with hands-on Labs

  • Fundamental concepts of AI, ML, and generative AI
  • Use cases of AI, ML, and generative AI
  • Design considerations for foundation models
  • Model Training and fine-tuning
  • Prompt engineering
  • Foundation model evaluation criteria
  • Responsible AI
  • Security and compliance for AI systems
1: Fundamentals of AI and ML20%
2: Fundamentals of Generative AI                                         24%
3: Applications of Foundation Models                                     28%
4: Guidelines for Responsible AI                                            14%
5: Security, Compliance, and Governance for AI Solutions14%