Foundations of LLMs
Transformer basics, scaling laws, architecture.
Professional Diploma · CLLME
Master LLM Engineering From the Ground Up
A complete diploma program that teaches you how to build, train, evaluate, and deploy large language models.
- 12 technical modules
- Build your own transformer-based LLM
- Capstone + final exam
- Diploma + transcript
- Verification registry entry
Social proof
Trusted by Engineers, Developers, and AI Teams
"This diploma finally explained transformers in a way that clicked. The capstone changed my career."
"We used this program to train our entire department. The ROI was immediate."
"The best structured LLM engineering curriculum I've seen outside of a university."
Curriculum
What You Will Learn
Tokenization & Embeddings
BPE, WordPiece, positional encodings, vector spaces.
Attention & Transformers
Self-attention, multi-head attention, QKV math.
Training & Optimization
Loss functions, batching, GPUs, distributed training.
Fine-Tuning & RLHF
LoRA, QLoRA, instruction tuning, reward models.
Evaluation & Deployment
Perplexity, safety tests, quantization, serving.
Scaling & Production
Sharding, ZeRO, DeepSpeed, caching, cost control.
Capstone
Your Capstone: Build a Working LLM
You will design, train, evaluate, and deploy a transformer-based language model tailored to a domain of your choice. This project becomes your portfolio centerpiece and proof of real engineering skill.
Start Your Capstone- Custom tokenizer
- Transformer architecture
- Training pipeline
- Evaluation metrics
- Deployment-ready inference script
- Technical report
Learning outcomes
What You Will Be Able To Do
Every outcome below is assessed in the capstone and final exam. These are the verifiable skills your diploma certifies.
- Design and implement a transformer architecture from scratch — tokenizer, embeddings, multi-head attention, and training loop.
- Fine-tune open-weight LLMs using LoRA, QLoRA, and instruction-tuning techniques on consumer GPUs.
- Evaluate model quality with perplexity, task benchmarks, and safety/red-team tests appropriate for production.
- Deploy quantized models behind low-latency inference servers with caching, batching, and cost controls.
- Identify and harden against the most common LLM agent exploits (prompt injection, tool abuse, data exfiltration).
- Communicate model behaviour, limits, and risk to non-technical stakeholders in writing.
How you are graded
Capstone Grading Rubric
The capstone is graded on six published criteria. Pass mark: 70% weighted total. Resubmission allowed once at no additional cost.
| Criterion | Weight | What we look for |
|---|---|---|
| Architecture correctness | 20% | Transformer blocks, attention math, and tokenizer behave as specified. |
| Training pipeline | 20% | Reproducible data prep, batching, optimizer schedule, and loss curves. |
| Evaluation rigor | 15% | Quantitative metrics + qualitative samples + at least one safety test. |
| Deployment readiness | 15% | Quantization, inference script, latency/cost notes, error handling. |
| Security & robustness | 15% | Documented threat model and mitigations for prompt injection / abuse. |
| Technical report | 15% | Clear writing, honest limitations, reproducibility instructions. |
How we compare
GACS vs Stanford, MIT, Coursera & Udemy
We benchmarked the CLLME diploma against the most respected LLM and deep-learning programs available today. Here is the honest, line-by-line comparison — what you get, what they get, and where we go further.
| Feature | GACS CLLME $499 lifetime | Stanford CS224N Free (audit) / $4k+ (SCPD) | MIT 6.S191 Free (OCW) | Coursera DeepLearning.AI $49–79/mo | Udemy LLM Courses $15–200 |
|---|---|---|---|---|---|
| Total instruction hours | 120+ hrs (12 modules) | ~40 hrs (20 lectures) | ~12 hrs (6 lectures) | ~60 hrs (5 courses) | 10–30 hrs typical |
| Build LLM from scratch | Yes — full transformer | Partial (assignments) | Demo notebooks only | Guided notebooks | Rarely |
| Fine-tuning (LoRA / QLoRA / RLHF) | All three, hands-on | LoRA covered | Mentioned | LoRA + RLHF | Varies wildly |
| Evaluation & benchmarking | Dedicated module + rubric | Lecture coverage | Brief | Course-level | Usually skipped |
| Production deployment | Full module (vLLM, quantization, cost) | Not covered | Not covered | Light | Some courses |
| Security & adversarial AI | Dedicated module + real GACS case data | Not covered | Not covered | Not covered | Not covered |
| Real-world fraud datasets | Yes — from GACS reports | Academic only | Academic only | Synthetic | Synthetic |
| Capstone project | End-to-end LLM, graded by rubric | Open project | Optional lab | Mini-projects | None typically |
| Final exam (proctored-style) | 50-question graded exam | Course exam | None | Quizzes | None |
| Diploma + transcript | Yes — printable PDF | Certificate (SCPD paid) | Certificate of completion | Specialization cert | Completion cert (low signal) |
| Public verification registry | Yes — public URL per diploma | No | No | Credly badge | No |
| Instructor support | Direct from the GACS instructor team | TAs + forums | Video only | Forums + mentors | Q&A board |
| Adaptive learning + XP/badges | Yes — gamified path | No | No | No | No |
| Accreditation path | CPD UK pending (free upgrade) | Institutional | Institutional | Partner universities | None |
| Cost (full program) | $499 lifetime | Free audit / $4,000+ SCPD | Free (no cert by default) | $240–$480 (4–6 mo @ $49–79) | $15–$200 per course |
What only we offer
- Real-world AI-fraud case data from GACS reports
- Dedicated security & adversarial-AI module
- Production deployment + cost optimization
- Public verification registry for your diploma
Where they win
- Stanford & MIT brand prestige (and 100+ year history)
- Existing institutional accreditation
- Larger alumni networks
- On-campus options (SCPD, on-site MIT)
Value verdict
At $499 you get roughly 8–10× the practical content of a Udemy course, the same technical depth as Stanford CS224N, plus security, deployment, and a verifiable diploma that none of the others issue at this price.
Pricing
Tuition: $499 USD
A complete professional diploma at a fraction of the cost of bootcamps or university programs.
Early Enrollment Guarantee · Lock in $499
Enroll now at $499 — get the accredited diploma free when accreditation completes.
We are actively pursuing CPD UK accreditation and partnerships with major institutions. Here's our promise to early students:
- Enroll today for $499 — same course material, same capstone, same instructor team, same diploma issued on completion.
- Free upgrade when accreditation lands. Once CPD UK / partner accreditation is approved, we email you the upgraded accredited diploma to print — at no extra cost. Swap it for your issued one, or keep both.
- Lifetime access at $499. One-time tuition, no subscription, no upsell. Future accreditation upgrade included free for enrolled students.
- No risk. Same skills, same portfolio, same graduation — whether accreditation arrives in 2 months or 6.
Rewarding the early students who help us prove demand to accreditation bodies and partners. Your enrollment literally helps fund the accreditation process.
Includes
- Full 12-module program
- 50-question final exam
- End-to-end LLM capstone
- Diploma + transcript
- Verification registry entry
- Badges + XP + adaptive learning
How it compares
- Udacity$1,200+
- MIT xPro$3,500+
- AI Bootcamps$2,000–$8,000
- Your Tuition$499
14-Day Satisfaction Guarantee
If the program isn't right for you, request a full refund within 14 days. No risk, no pressure.
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Corporate EnrollmentAuthoritative sources
Independent primary sources used to check and corroborate the guidance on this page.
NIST National Initiative for Cybersecurity Education (NICE)US national framework for cybersecurity work roles and competencies.
Financial Action Task Force (FATF)Global AML/CFT standards referenced by financial-crime curricula.
FBI Internet Crime Complaint Center — Annual ReportsPrimary loss and typology data used in fraud-investigation training.
US Federal Trade Commission — Consumer Sentinel Data BookOfficial complaint statistics for teaching fraud trend analysis.