Live Online Training
NLP with LLMs Course
Six intensive weeks on the highest-demand AI specialization of 2026. Transformer architecture, fine-tuning open-weight models, RAG systems with vector databases, and the LangChain / Hugging Face stack. Cohort capped at 18 so every learner gets direct feedback.Six intensive weeks on the highest-demand AI specialization of 2026. Transformer architecture, fine-tuning open-weight models, RAG systems with vector databases, and the LangChain / Hugging Face stack. Cohort capped at 18 so every learner gets direct feedback.
Live Online Training
6 Intensive Weeks
Fine-Tuning & RAG
Cohort Cap: 18 Students
Trusted by Learners & Professionals from Top Companies
WHY LEARN NLP WITH LLMs TODAY?
The Most In-Demand AI Specialization in 2026
- Every AI-first company in the US is hiring engineers who can ship LLM applications
- Fine-tuning, RAG, and evaluation — not just prompt engineering
- Six weeks of concentrated live instruction, no filler content
- Small cohort of 18 for direct instructor feedback on your actual work
- The direct pathway to LLM engineer, applied AI engineer, and NLP engineer roles
WHAT YOU'LL LEARN
Tokenization & Embeddings
Transformer Architecture Deep Dive
Fine-Tuning Large Language Models
RAG System Design
Vector Databases
LangChain & Hugging Face
LLM Evaluation Methods
Production NLP Engineering
TOOLS & FRAMEWORKS YOU'LL MASTER
Hugging Face Transformers
The open-source NLP backbone
LangChain
LLM application framework
LangGraph
Stateful agent orchestration
Pinecone
Managed vector database
Weaviate
Open-source vector DB with hybrid search
Chroma
Local vector DB for prototyping
PyTorch
Deep learning backend for training
PEFT / LoRA
Parameter-efficient fine-tuning
QLoRA
Quantized LoRA for larger models
OpenAI API
GPT models in production
Anthropic API
Claude for reasoning-heavy work
LlamaIndex
Data indexing for LLM apps
COURSE CURRICULUM
01
02
03
04
05
Week 1
NLP Pipeline & Tokenization
- Modern NLP workflow
- BPE, WordPiece, SentencePiece
- Embeddings and their trade-offs
- Foundation model landscape
- Cost, latency, capability trade-offs
Week 2
Transformer Deep Dive
- Self-attention from first principles
- Multi-head attention
- Positional encoding (sinusoidal, RoPE)
- Encoder, decoder, encoder-decoder
- Scaling laws and model size
Week 3
Fine-Tuning LLMs
- When to fine-tune vs prompt
- Supervised fine-tuning workflows
- LoRA and QLoRA
- Instruction and chat fine-tuning
- Quantization and inference
Week 4
RAG Systems
- Embedding models for retrieval
- Chunking strategies
- Pinecone, Weaviate, Chroma, pgvector
- Hybrid search and re-ranking
- Advanced RAG (HyDE, multi-step retrieval)
Week 5
LangChain, Hugging Face & Modern NLP Stack
- LangChain and LangGraph
- Hugging Face ecosystem
- Function calling in production
- Streaming and real-time UX
- Open-weight model deployment
What You Will Learn
The Six-Week Curriculum
Week 2 covers the transformer architecture at a depth beyond the introduction in Deep Learning & Neural Networks: self-attention from first principles, multi-head attention, positional encoding including RoPE, encoder/decoder/encoder-decoder variants, and scaling laws. Learners implement self-attention from scratch, then read production code in major open-weight model implementations.
You will Explore
Modern NLP Pipeline
Tokenization Deep Dive
Foundation Model Landscape
Transformer Architecture
Week 3 covers the fine-tuning landscape, which has expanded considerably with the rise of parameter-efficient methods. Topics include the crucial question of when to fine-tune versus when to prompt; supervised fine-tuning workflows; parameter-efficient fine-tuning (LoRA, QLoRA, prefix tuning) with hands-on work in the Hugging Face PEFT library; instruction and chat fine-tuning; a conceptual introduction to RLHF and direct preference optimization; and quantization for inference and training.
By the end of Week 3, every learner has fine-tuned at least one open-weight model on their own task using parameter-efficient methods.
You will Explore
Fine-Tune vs Prompt Decision
LoRA & QLoRA
Instruction Tuning
Quantization Techniques
Week 4 receives extended treatment because RAG is where the largest share of applied LLM work is currently happening. Topics include the RAG architecture in depth, embedding models for retrieval, vector databases (Pinecone, Weaviate, Chroma, Qdrant, pgvector), chunking strategies that determine whether RAG systems actually work, hybrid search combining semantic and keyword retrieval, cross-encoder re-ranking, and advanced RAG patterns including multi-step retrieval, query rewriting, and hypothetical document embeddings (HyDE).
You will Explore
Vector Databases at Scale
Chunking Strategies
Hybrid Search & Re-ranking
Advanced RAG Patterns
The final block covers the applied stack and production practices. Topics include LangChain and LangGraph (balanced coverage — used where they help, avoided where they get in the way); the Hugging Face ecosystem in depth; function calling and structured outputs in production; streaming and real-time UX; caching, batching, and cost engineering; open-weight model deployment; LLM evaluation methodology; and practical safety and guardrails.
LangChain & LangGraph
Hugging Face Ecosystem
LLM Evaluation
Safety & Guardrails
The capstone project is presented in the final session — a working LLM-powered application built end-to-end, integrating fine-tuning or RAG (or both), structured outputs, evaluation, and a basic deployment.
Capstone Project
Fine-Tune • Retrieve • Ship
A working LLM-powered application built end-to-end. Fine-tuning or RAG (or both), structured outputs, evaluation, and deployment. Recent capstones: specialized domain assistants, document analysis systems, internal knowledge bases, and customer-facing chat applications.
By the end of six weeks, you'll have shipped a real LLM system — the kind of portfolio piece the top US LLM engineering roles specifically look for.
REAL-WORLD PROJECTS
Custom Tokenizer Analysis
Compare tokenization across GPT, Claude, Llama on real text.
Fine-Tuned Domain Model
Adapt an open-weight model to your domain with LoRA.
Production RAG System
End-to-end retrieval with hybrid search and re-ranking.
Structured Output Pipeline
Reliable data extraction from unstructured documents.
Full LLM Application
Capstone: fine-tuning + RAG + evaluation + deployment.
Course Format and Delivery
The course is delivered fully online through live, instructor-led sessions taught by a working NLP and LLM practitioner from the US technology market.
01
Schedule
- Each session runs approximately 90 minutes.
02
Live Coding & Model Work
Sessions are heavily applied. Learners write code, train models, and build systems during sessions.
- Learn by building.
03
Cohort Cap: 18 Students
The smaller cap (versus the bootcamp’s 20) reflects the more intensive nature of the material.
- Small cohort. Direct feedback.
04
GPU Access Included
Compute for fine-tuning exercises provided through a managed GPU environment. No hardware purchases needed.
- Real fine-tuning. No hardware headaches.
05
Code Reviews on Capstone
Capstone work receives direct, detailed code review with written feedback.
- Ship-quality feedback.
Prerequisites and Technical Requirements
Prerequisites for this course are genuine. Learners who do not meet them struggle.
ML foundation — supervised learning, evaluation, standard workflows (ML Bootcamp recommended)
Deep learning background — neural networks, training dynamics, transformer intro (DL course recommended)
Strong Python with async programming basics
Plan for 10–15 hours per week (3 live sessions + independent practice)
Why NLP With LLM Skills Matter In The Us Job Market
The Strongest AI Labor Market Right Now.
The market for NLP and LLM engineers in the US is one of the strongest in technology. Job titles requiring the skill set this course teaches include NLP engineer, LLM engineer, AI engineer, applied scientist (NLP focus), and generative AI engineer.
Compensation That Reflects the Demand.
Entry-level positions at AI-focused companies commonly start in the low-to-mid six-figure range, mid-career reaches high six-figures, and senior practitioners at frontier labs and top employers reach into seven figures. Demonstrated capability — not credentials — unlocks the higher ranges.
Beyond Prompt Engineering.
Most professionals with "AI experience" can prompt. Few can fine-tune, build production RAG systems, and evaluate LLM output rigorously. This course teaches the specialization that separates senior LLM engineers from prompt engineers.
Learn From Industry Leaders
Michael Anderson
Lead Instructor — AI & Cloud
- AWS Certified
- 10+ Years Industry Experience
- Published AI Researcher
Senior AI engineer with extensive experience designing machine learning platforms and deploying enterprise AI solutions.
David Miller
Cloud & MLOps Instructor
- Cloud Architect
- Google Cloud Certified
- Enterprise MLOps Specialist
Cloud Architect specializing in scalable AI infrastructure, MLOps automation, and multi-cloud deployments for enterprise.
INDUSTRY RECOGNIZED CERTIFICATE
Receive an industry-recognized certificate after successful course completion.
- Industry Recognized
- LinkedIn Shareable
- Verifiable Certificate
- Career Boost
Career Impact
What Our Learners Say
The AI for Data Analytics course gave me the exact tools needed to interpret complex data streams quickly. The practical framework for integrating modern AI directly into existing analytical workflows was immediately applicable.
The multi-agent simulations in the Advanced Python for LLM & Multi Agent Systems course completely transformed our engineering team's approach to complex system design. Exceptional depth and real-world utility.
Enrolling our product team in Agentic AI & Multi Agent Autonomous Systems provided the strategic edge we were missing. The hands-on project structures made deploying autonomous agents far more straightforward.
After taking NLP with LLMs, I was able to fine-tune and deploy a custom retrieval-augmented model in weeks. The instructors cover production-grade deployment strategies that you rarely find in standard online courses.
The Executive Diploma in Deep AI & Cloud Intelligence offered a clear, high-level strategic overview without glossing over technical rigor. It helped our leadership team align our enterprise cloud architecture with modern AI solutions.
Bridging the gap between prototype models and production infrastructure can be tough, but MLOps & AI in Production laid out the best practices clearly. Highly recommended for any engineering group scaling AI services.
Frequently Asked Questions
The two are complementary. Advanced Python for LLM focuses on the software engineering side — APIs, async, agent frameworks, production engineering. NLP with LLMs focuses on the language-modeling and fine-tuning side — tokenization, transformer architecture, LoRA/QLoRA fine-tuning, RAG depth, and NLP-specific evaluation. Many learners take both, in either order, to build full-stack LLM engineering capability.
Yes. Week 3 is dedicated to fine-tuning, including LoRA and QLoRA that make it practical without industrial compute. Every learner fine-tunes at least one open-weight model on their own task.
Both. Application-building portions use API-based models (GPT, Claude, Gemini). Fine-tuning portions use open-weight models (Llama, Mistral, Qwen). Production LLM systems combine them frequently.
For most learners, yes. The transformer material and fine-tuning workflows assume working understanding of neural network training and optimization. Learners with equivalent DL experience from work or self-study can sometimes go directly.
Pinecone is the headline. Weaviate, Chroma, Qdrant, and pgvector are also covered. Selection reflects what's actually used in US production deployments.
Fundamentals (transformer architecture, fine-tuning methods, RAG patterns, evaluation) are stable. Specific models, framework versions, and tools are updated each cohort. The course does not chase every new release.
Hiring teams in this space evaluate candidates primarily on demonstrated technical work — capstones, code samples, technical depth in interviews. The course is designed to produce work that holds up to that evaluation.
Start Your LLM Engineering Journey!
Cohort cap 18. Six intensive weeks. Seats fill quickly.
- Live Intensive Cohort
- Fine-Tuning Included
- Production RAG Systems
- Practitioner Instructor