AI+ Engineer (AI+ Engineer)

• Foundational Understanding
• Ethical Considerations
• Understanding AI Architecture and Development Lifecycle
• Practical Implementation and Hands-on Experience
• Effective Communication and Deployment

Required

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

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

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

Recommended

• AI+ Executive
• AI+ Developer
• AI+ Data

• AI Engineer
• IT Professional

Foundations of Artificial Intelligence

• Introduction to AI
• Core Concepts and Techniques in AI
• Ethical Considerations

Introduction to AI Architecture

• Overview of AI and its Various Applications
• Introduction to AI Architecture
• Understanding the AI Development Lifecycle
• Hands-on: Setting up a Basic AI Environment

Fundamentals of Neural Networks

• Basics of Neural Networks
• Activation Functions and Their Role
• Backpropagation and Optimization Algorithms
• Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

Applications of Neural Networks

• Introduction to Neural Networks in Image Processing
• Neural Networks for Sequential Data
• Practical Implementation of Neural Networks

Significance of Large Language Models (LLM)

• Exploring Large Language Models
• Popular Large Language Models
• Practical Finetuning of Language Models
• Hands-on: Practical Finetuning for Text Classification

Application of Generative AI

• Introduction to Generative Adversarial Networks (GANs)
• Applications of Variational Autoencoders (VAEs)
• Generating Realistic Data Using Generative Models
• Hands-on: Implementing Generative Models for Image Synthesis

Natural Language Processing

• NLP in Real-world Scenarios
• Attention Mechanisms and Practical Use of Transformers
• In-depth Understanding of BERT for Practical NLP Tasks
• Hands-on: Building Practical NLP Pipelines with Pretrained Models

Transfer Learning with Hugging Face

• Overview of Transfer Learning in AI
• Transfer Learning Strategies and Techniques
• Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks

Crafting Sophisticated GUIs for AI Solutions

• Overview of GUI-based AI Applications
• Web-based Framework
• Desktop Application Framework

AI Communication and Deployment Pipeline

• Communicating AI Results Effectively to Non-Technical Stakeholders
• Building a Deployment Pipeline for AI Models
• Developing Prototypes Based on Client Requirements
• Hands-on: Deployment

Optional Module: AI Agents for Engineering

• Understanding AI Agents
• Case Studies
• Hands-On Practice with AI Agents