AI+ Engineer

The AI+ Engineer certification program offers a structured journey through the foundational principles, advanced techniques, and practical applications of Artificial Intelligence (AI). Beginning with the Foundations of AI, participants progress through modules covering AI Architecture, Neural Networks, Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), and Transfer Learning using Hugging Face. With a focus on hands-on learning, students develop proficiency in crafting sophisticated Graphical User Interfaces (GUIs) tailored for AI solutions and gain insight into AI communication and deployment pipelines. Upon completion, graduates are equipped with a robust understanding of AI concepts and techniques, ready to tackle real-world challenges and contribute effectively to the ever-evolving field of Artificial Intelligence.

Upon successful completion of AI+ Engineer certification, participants will attain a comprehensive understanding of Artificial Intelligence (AI) fundamentals, ranging from the foundational principles to advanced applications. Through modules focusing on AI architecture, neural networks, Large Language Models (LLMs), generative AI, and Natural Language Processing (NLP), students will gain hands-on experience in building and deploying AI solutions. They will harness Transfer Learning techniques using frameworks like Hugging Face, enabling them to adapt pre-trained models for various tasks efficiently. Furthermore, participants will develop the skills to craft sophisticated Graphical User Interfaces (GUIs) tailored specifically for AI applications. By the course's conclusion, learners will possess the knowledge and proficiency necessary to navigate AI communication and deployment pipelines, ensuring successful integration and utilization of AI technologies in diverse contexts.

Prerequisites

• AI+ Data or AI Developer course should be completed.

• Basic understanding of Python

• Python Programming: Proficiency in Python is mandatory for hands-on exercises and project work.

• Basic Math: Familiarity with high school-level algebra and basic statistics.

• Computer Science Fundamentals: Understanding basic programming concepts (variables, functions, loops) and data structures (lists, dictionaries).

Exams

• Number of Questions: 50

• Passing Score: 35/50 or 70%

• Duration: 90 Minutes

• Format: Online via AI Proctoring platform

• Question Type: Multiple Choice/Multiple Response

Exam Overview:

• Foundations of Artificial Intelligence - 5%

• Introduction to AI Architecture - 10%

• Fundamentals of Neural Networks - 15%

• Applications of Neural Networks - 7%

• Significance of Large Language Models (LLM) - 8%

• Application of Generative AI - 8%

• Natural Language Processing - 15%

• Transfer Learning with Hugging Face - 15%

• Crafting Sophisticated GUIs for AI Solutions - 10%

• AI Communication and Deployment Pipeline - 7%