About the Course:
This 3-hour intensive AI Engineer Bootcamp provides a hands-on approach to understanding and building AI-driven solutions. Participants will gain insights into modern AI frameworks, model deployment strategies, and real-world use cases. We will explore AI tools like TensorFlow, PyTorch, OpenAI's GPT models, and MLOp's best practices to prepare you for an AI engineering career in 2025 and beyond.
Course Objective:
- Understand AI engineering fundamentals, ML, and deep learning concepts.
- Get hands-on exposure to AI frameworks (TensorFlow, PyTorch).
- Learn about LLMs (Large Language Models) and prompt engineering.
- Explore model deployment strategies with cloud and edge computing.
- Understand MLOps workflows and best practices.
Who is the Target Audience?
- Aspiring AI Engineers & Data Scientists
- Software Engineers & Developers
- Machine Learning Enthusiasts
- AI Product Managers & Tech Leaders
- Students & Professionals seeking AI career transition
Basic Knowledge:
- Basic Python programming knowledge
- Familiarity with Machine Learning concepts (preferred but not mandatory)
- Interest in AI/ML tools and frameworks
Curriculum
Total Duration: 3 Hours
Introduction to AI Engineering
AI vs ML vs Deep Learning
Role of an AI Engineer in 2025
Machine Learning & Deep Learning Fundamentals
Supervised vs Unsupervised Learning
Neural Networks & Deep Learning Essentials
Hands-on with AI Frameworks
TensorFlow vs PyTorch: Which one to use?
Building a Simple Neural Network Model
Large Language Models (LLMs) & Generative AI
Understanding GPT-4, GPT-5, and Beyond
Prompt Engineering & Fine-tuning LLMs
Model Deployment & AI in Production
Edge AI & Real-world Applications
MLOps: Best Practices for AI Engineering
CI/CD for ML models
Automating ML workflows with MLOps Tools