Best Fit
- Complete beginners who need a structured path into AI engineering.
- Learners with partial background who want to close foundation gaps before advanced work.
- Professionals who want one integrated diploma, not fragmented short courses.
Flagship Diploma - Full Pathway
AI Systems Engineering Diploma: NLP, RAG and Agents
Built for learners who want a complete path from foundations to production AI systems. Start from beginner-friendly Python and progress to deploying evaluated RAG and agent-based applications with confidence.
24 Weeks
Program Length
48 Live Sessions
2 Per Week
120 Guided Hours
Practical Delivery
Portfolio + Defense
Graduation Standard
Build End-to-End AI Products
From data preparation to deployed interfaces, students build complete systems rather than isolated model demos.
Graduate with a Defensible Portfolio
Your outputs include evaluated RAG systems, workflow/agent builds, and a final architecture defense with evidence.
Depth That Short Tracks Cannot Cover
The full diploma includes foundational training, deployment bridge, and specialization in one coherent sequence.
Technology Environment
Core stack covered across the full diploma journey:
Python and Data Stack
NumPy, Pandas, scikit-learn
LLM and Adaptation
PyTorch, Hugging Face, LoRA/QLoRA
RAG and Evaluation
Retrieval pipelines, benchmark harnesses
Production Delivery
FastAPI, Docker, Streamlit, MCP integration
Who This Diploma Is For
Use this section to self-select before applying. It helps you choose the right path from day one.
Not the Best Fit
- Developers already strong in Python, ML basics, and deployment who want faster specialization.
- Learners looking only for prompt usage or no-code automation workflows.
- Students unable to commit to a 24-week progression with weekly practical delivery.
If You Already Have Foundations
If you already meet prerequisites, the shorter advanced specialization track may be the better route.
- Full diploma: foundations + deployment + specialization.
- Advanced track: specialization for already-prepared learners.
Diploma vs Short Advanced Track
Clear product architecture so applicants can choose based on readiness, not guesswork. The homepage may highlight the advanced entry path separately for prepared learners, while this page defines the full flagship diploma pathway.
AI Systems Engineering Diploma
- 24 weeks, 48 live sessions, full progression.
- Starts from foundations and builds up to production specialization.
- Best for learners who want one complete pathway.
NLP, RAG and Agents Specialization
- Shorter path focused on advanced NLP system engineering.
- Assumes prior Python and ML readiness.
- Best for experienced learners who need only specialization depth.
Roadmap: Overview to Execution
Structured in three levels: diploma overview, phase summary, then practical week-by-week breakdown.
Program Overview
24-week diploma that moves from foundations to deployment and then full NLP/LLM specialization.
- Weekly format: 2 live sessions.
- Exact weekly guided learning time: 5 hours (3-hour session + 2-hour session).
- Graduation requires capstone demo plus technical defense.
Progression Logic
Each phase prepares the next. Students are not pushed into advanced modules before they are ready.
- Phase 1: foundations in Python, ML, DL.
- Phase 2: deployment bridge and production setup.
- Phase 3: NLP, RAG, agents, MCP, capstone.
Outcome Focus
The program is portfolio-oriented and evidence-driven.
- Multiple intermediate deliverables, not only a final project.
- Evaluation continuity from early RAG builds to final capstone.
- Production packaging, documentation, and presentation readiness.
Foundations
Python, data analysis, machine learning, and deep learning foundations.
Output: baseline technical fluency and foundational mini-capstone artifacts.
Deployment Bridge
Hugging Face workflows, FastAPI services, Docker packaging, and UI integration.
Output: deployment-ready mini service and operational packaging habits.
Specialization
Classical NLP, LLM engineering, RAG, agents, MCP integration, and capstone defense.
Output: portfolio-grade AI system with live demo, metrics, and architecture defense.
Weekly Breakdown
Digestible Week Groups
Weeks 1-4 | Python and Data Foundations
Programming fluency, structured data handling, exploratory analysis, and visualization discipline.
- Week 1-2: Python fundamentals, functions, data structures, NumPy essentials.
- Week 3-4: Pandas workflows, cleaning, EDA, visual storytelling.
Weeks 5-8 | Applied ML Foundations
Classification, validation, regression diagnostics, and honest model comparison.
- Week 5-6: ML workflow, leakage prevention, evaluation metrics, decision trees.
- Week 7-8: Regression, bias-variance, ensembles, clustering, PCA, mini-capstone.
Weeks 9-10 | Deep Learning Foundations
Neural network mechanics, PyTorch training loops, CNN intuition, and transfer learning.
- Week 9: backpropagation, optimization, debugging training behavior.
- Week 10: architecture comparison and transfer-learning mini-project.
Weeks 11-13 | Deployment Bridge
Serving models as products, packaging reproducible systems, and connecting interfaces.
- Week 11: model serving and Hugging Face workflow.
- Week 12: FastAPI architecture and robust endpoint design.
- Week 13: Docker packaging, UI connection, and deployment checks.
Weeks 14-19 | NLP, LLM, and RAG Core
Classical NLP foundations then progression into LLM engineering and measured RAG systems.
- Week 14-15: classical NLP, sequence model evolution, evaluated project.
- Week 16: controlled LLM application design and reliability engineering.
- Week 17-18: baseline then advanced RAG with retrieval improvement.
- Week 19: full evaluation harness and failure taxonomy discipline.
Weeks 20-24 | Adaptation, Agents, MCP, Capstone
Fine-tuning decisions, workflow orchestration, agent architecture, MCP integration, and final defense.
- Week 20: LoRA/QLoRA adaptation and pre/post evaluation.
- Week 21-22: workflow orchestration, LangChain, and LangGraph agents.
- Week 23: MCP server integration with resilient tool use.
- Week 24: capstone integration sprint, live demo, and technical defense.
Not Sure If This Path Fits You?
Before applying, you can speak with admissions to confirm your readiness and decide whether the full diploma or the shorter advanced track is the better fit.
Admissions guidance is for pathway selection and readiness alignment. It is not an employment guarantee.
What Graduation Looks Like
Graduation is not a single demo. It is a portfolio package that shows practical execution quality and decision maturity.
System Build
- Working AI application integrating retrieval and tool workflows.
- Reproducible run path and deployment-ready project structure.
- Clear README that another engineer can follow.
Evaluation Evidence
- Metric tables showing baseline and improved system behavior.
- Failure analysis notes with remediation rationale.
- Consistent harness usage from earlier phases into capstone.
Defense Readiness
- Architecture diagram with explicit tradeoffs.
- Live walkthrough script for technical presentation.
- Capstone defense focused on clarity, reliability, and scope control.
Portfolio and Graduation Outcomes
By graduation, students leave with a coherent set of artifacts that demonstrate progression from fundamentals to production systems.
Foundation Evidence
- Structured notebooks for Python, ML, and deep learning milestones.
- Mini-capstone outputs showing baseline modeling discipline.
- Evaluation-aware reports, not only code submissions.
Production Engineering Evidence
- Deployment bridge application with API, UI, and Docker packaging.
- Structured LLM service and measurable RAG system iterations.
- Evaluation harness extended across versions and experiments.
Specialization Evidence
- Fine-tuning report with clear pre/post task comparison.
- Workflow and agent implementations with reliability notes.
- MCP-integrated capstone with architecture diagram, README, and live defense.
Graduate transformation: from learner to practitioner who can scope, build, evaluate, and present production AI systems responsibly.
24-Week Flagship Diploma
One coherent pathway from beginner foundations to advanced specialization
48 Live Sessions
2 sessions per week: one 3-hour session + one 2-hour session
Diploma + Portfolio Credential
Capstone demo and technical defense required for graduation