Advanced AI and ML Courses

Learning Objectives: Describe transformer architecture; LLM workflow.
Key Topics: Sequence modeling, pre-training, fine-tuning.
Activities: LLM lab, transformer demo.

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MODULE 1: Foundations of AI, Machine Learning, & Python Programming
(Days 1–30 / Classes 1–12)
12
MODULE 2: Generative AI, Deep Q-Learning, LLMs, & Prompt Engineering
(Days 31–60 / Classes 13–24)
12
MODULE 3: Blockchain, Data Science for Business, & Digital Marketing
(Days 61–90 / Classes 25–36)
12
Conclusion
This comprehensive, modular curriculum delivers a transformative learning journey across AI, generative models, machine learning, Python, blockchain, and digital marketing. Learners will graduate prepared to apply advanced technical and business skills, drive innovation, and lead in the tech industry. Each class leverages interactive activities, hands-on labs, and targeted learning objectives—ensuring students master critical concepts and real-world application for professional and career excellence.
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Class 15: Large Language Models (LLMs): Structure & Function

Summary: Mechanics of LLMs in text processing—transformers, attention.