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Package AI apps into Streamlit UIs, deploy on public cloud (AWS/GCP), implement LLMOps monitoring with LangSmith, establish CI/CD for AI, and understand LLMOps as a discipline.
Build AI agents from first principles, design stateful LangGraph workflows, implement all major agent patterns compare CrewAI vs AutoGen, and ship a production multi-agent e-commerce support system.
Decide when fine-tuning beats prompting and RAG, implement LoRA/QLoRA, prepare high-quality datasets, evaluate models, and deploy fine-tuned adapters to production.
Understand why RAG solves hallucination and knowledge-cutoff problems, build complete RAG pipelines with embeddings and vector stores, and implement advanced retrieval patterns.
Build a systematic approach to prompting, master all major frameworks from zero-shot to chain-of-thought, decide when to prompt vs RAG vs fine-tune, and build reusable prompt libraries.
Master NLP fundamentals, build neural network intuition from MLP to LSTM, follow the evolutionary path through Attention to Transformers, and understand how LLMs are trained.
From human intelligence to Artificial Intelligence to GenerativeAI — the big picture
Explaining the past, predicting the future — Data Mining Map Apply statistical exploration to real data, refresh the Maths underpinning ML/DL, and understand and utilize core supervised, unsupervised, and heuristic learning algorithms
Manipulate numerical arrays with NumPy, wrangle structured data with Pandas, and create insightful visualisations with Matplotlib — the three pillars of every data science pipeline.
Gain fluency in Python syntax, all major data structures, control flow, and functional programming patterns — the essential toolkit for every data science and AI workflow.
