Innovate Engineering: Leverage AI-Driven Smart Solutions Full AI Stack: Learn AI architecture, LLMs, NLP, and neural networks Tool Proficiency: Includes Transfer Learning with Hugging Face and GUI design Deployment Focus: Build real AI systems and manage communication pipelines Practical Mastery: Gain the skills to engineer scalable AI solutions for innovation

• AI & Software Engineers: Enhance your development skills by mastering AI techniques and designing advanced AI systems.
• Machine Learning Enthusiasts: Apply deep learning, neural networks, and NLP techniques to real-world AI challenges.
• Data Scientists: Strengthen your AI toolkit with engineering techniques for building and deploying scalable AI solutions.
• IT Specialists & System Architects: Integrate AI solutions into existing infrastructures, optimizing performance and scalability.
• Students & New Graduates: Develop in-demand AI engineering skills and prepare for a successful career in the rapidly growing AI field.

• AI & Software Engineers: Enhance your development skills by mastering AI techniques and designing advanced AI systems.
• Machine Learning Enthusiasts: Apply deep learning, neural networks, and NLP techniques to real-world AI challenges.
• Data Scientists: Strengthen your AI toolkit with engineering techniques for building and deploying scalable AI solutions.
• IT Specialists & System Architects: Integrate AI solutions into existing infrastructures, optimizing performance and scalability.
• Students & New Graduates: Develop in-demand AI engineering skills and prepare for a successful career in the rapidly growing AI field.

• 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

Course Introduction

Module 1: Foundations of Artificial Intelligence
• 1.1 Introduction to AI
• 1.2 Core Concepts and Techniques in AI
• 1.3 Ethical Considerations

Module 2: Introduction to AI Architecture
• 2.1 Overview of AI and its Various Applications
• 2.2 Introduction to AI Architecture
• 2.3 Understanding the AI Development Lifecycle
• 2.4 Hands-on: Setting up a Basic AI Environment

Module 3: Fundamentals of Neural Networks
• 3.1 Basics of Neural Networks
• 3.2 Activation Functions and Their Role
• 3.3 Backpropagation and Optimization Algorithms
• 3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

Module 4: Applications of Neural Networks

Module 5: Significance of Large Language Models (LLM)
• 5.1 Exploring Large Language Models
• 5.2 Popular Large Language Models
• 5.3 Practical Finetuning of Language Models
• 5.4 Hands-on: Practical Finetuning for Text Classification

Module 6: Application of Generative AI
• 6.1 Introduction to Generative Adversarial Networks (GANs)
• 6.2 Applications of Variational Autoencoders (VAEs)
• 6.3 Generating Realistic Data Using Generative Models
• 6.4 Hands-on: Implementing Generative Models for Image Synthesis

Module 7: Natural Language Processing
• 7.1 NLP in Real-world Scenarios
• 7.2 Attention Mechanisms and Practical Use of Transformers
• 7.3 In-depth Understanding of BERT for Practical NLP Tasks
• 7.4 Hands-on: Building Practical NLP Pipelines with Pretrained Models

Module 8: Transfer Learning with Hugging Face
• 8.1 Overview of Transfer Learning in AI
• 8.2 Transfer Learning Strategies and Techniques
• 8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks

Module 9: Crafting Sophisticated GUIs for AI Solutions
• 9.1 Overview of GUI-based AI Applications
• 9.2 Web-based Framework
• 9.3 Desktop Application Framework

Module 10: AI Communication and Deployment Pipeline
• 10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
• 10.2 Building a Deployment Pipeline for AI Models
• 10.3 Developing Prototypes Based on Client Requirements
• 10.4 Hands-on: Deployment
• Optional Module: AI Agents for Engineering
• 1. Understanding AI Agents
• 2. Case Studies
• 3. Hands-On Practice with AI Agents