AI+ Engineer™

• AI & Software Engineers  

• Machine Learning Engineers  

• Data Scientists & Developers  

• IT System Architects & DevOps Engineers  

• Graduates aiming for AI system careers  

• AI System Architecture  

• Deep Neural Networks  

• Transfer Learning (Hugging Face)  

• Natural Language Processing (NLP)  

• GUI & Front-End Integration  

• Communication Pipeline Management  

• Scalable AI Solution Deployment 

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

• Basic understanding of Python programming is mandatory for hands-on exercises and project work.

• Familiarity with high school-level algebra and basic statistics is required.

• Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential.

Course Overview: An introduction to the field of AI engineering, its importance in modern technology, and the career paths available to certified professionals.

• Module 1: Foundations of Artificial Intelligence: A foundational understanding of AI, machine learning, and deep learning, including key concepts, history, and applications.

• Module 2: Introduction to AI Architecture: Learn the core components and design principles of AI systems, from data ingestion to model serving. This module will cover the different layers and workflows involved in building a complete AI solution.

• Module 3: Fundamentals of Neural Networks: A deep dive into the building blocks of modern AI. You will learn about neurons, layers, activation functions, and how to build a basic neural network from scratch.

• Module 4: Applications of Neural Networks: Explore the diverse applications of neural networks in areas such as computer vision, natural language processing, and time-series forecasting.

• Module 5: Significance of Large Language Models (LLM): Understand the power and potential of Large Language Models, including their architecture, training process, and the core concepts that make them so effective.

• Module 6: Application of Generative AI: Get hands-on with generative AI. This module will cover how to use and fine-tune models to create new content, such as text, images, and code, and explore the ethical considerations of this technology.

• Module 7: Natural Language Processing: Learn the fundamental techniques of Natural Language Processing (NLP), including tokenization, sentiment analysis, and text classification, which are essential for building intelligent language-based applications.

• Module 8: Transfer Learning with Hugging Face: A practical guide to using the Hugging Face Transformers library. You will learn how to leverage pre-trained models to solve new tasks with minimal training data, a key skill for efficient and high-performing AI solutions.

• Module 9: Crafting Sophisticated GUIs for AI Solutions: Master the art of creating user-friendly interfaces for AI applications. This module will cover tools and frameworks for building interactive and intuitive Graphical User Interfaces (GUIs), such as Streamlit or Gradio, to make your AI models accessible to end-users.

• Module 10: AI Communication and Deployment Pipeline: Learn how to effectively communicate the results of your AI models and deploy them to production. This module will cover best practices for MLOps (Machine Learning Operations), including version control, model monitoring, and building a CI/CD (Continuous Integration/Continuous Deployment) pipeline for AI.

Optional Module: AI Agents for Engineering:

• What Are AI Agents: An introduction to the concept of AI agents as autonomous systems that can perceive their environment, make decisions, and perform multi-step tasks to achieve a goal.
• Key Capabilities of AI Agents in Engineering: Explore how AI agents are used to automate repetitive engineering tasks, such as CAD modeling, simulation analysis, and report generation.
• Applications and Trends for AI Agents in Engineering: A look at the real-world applications and future trends of AI agents in various engineering fields, from software development to civil and mechanical engineering.