Course Overview
Unpack the revolutionary field of AI. Understand how modern neural networks work, explore supervised and unsupervised learning algorithms, discover generative AI APIs (LLMs, vision models), and build smart applications using Python and popular AI toolkits.
Course Curriculum & Modules
01Module 1: Foundations of Artificial Intelligence & Machine Learning
02Module 2: Python Data Science Toolkits (NumPy, Pandas)
03Module 3: Supervised & Unsupervised Learning Algorithms
04Module 4: Generative AI, Prompt Engineering & LLM APIs
05Module 5: Building a Practical Smart AI Application
Software Engineering Department Pathway
Software Engineering covers applied AI/ML integration, full-stack web/mobile application architecture, DevOps containerization (Docker/Kubernetes), cloud security, high-throughput microservices, and enterprise architecture.
Computer & Hardware Requirements
Processor:Multi-core processor (Intel i5/i7 or AMD Ryzen 5/7)
Memory (RAM):16 GB RAM recommended
Graphics Card:NVIDIA GPU recommended for local model inference (optional)
Storage:100 GB SSD storage
Display:1080p display
Key Learning Outcomes & Benefits
- ✓Understand AI core concepts, Machine Learning pipelines, and Deep Learning.
- ✓Use Python, Scikit-learn, and PyTorch for basic model training.
- ✓Integrate state-of-the-art AI APIs into modern web/mobile applications.
- ✓Analyze ethical considerations and data privacy in AI deployment.