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LLM Fundamentals
This comprehensive course takes you from the basics of neural networks to mastering Large Language Models. You'll understand how transformers work, learn effective prompt engineering techniques, and explore fine-tuning strategies for domain-specific applications.
14 lessons7 hoursBeginner
Course in Development
New lessons are published every week. Follow me to get notified when a new lesson is published.
What You'll Learn
How transformer architecture enables language understanding
Embeddings, semantic search, and vector representations
Prompt engineering techniques for reliable outputs
Fine-tuning strategies including LoRA and PEFT
Function calling and MCP for tool integration
Build a production-ready LLM application from scratch
Course Syllabus
1
Introduction to Language ModelsAvailable19 min
2
The Transformer ArchitectureAvailable36 min
3
Attention Mechanisms Explained35 min
4
Tokenization Deep Dive30 min
5
Embeddings & Semantic Search30 min
6
Prompt Engineering Fundamentals45 min
7
Advanced Prompting Techniques40 min
8
Fine-tuning Strategies50 min
9
LoRA and Parameter-Efficient Fine-tuning35 min
10
Evaluation and Benchmarking30 min
11
Cost Optimization25 min
12
Function Calling & MCP30 min
13
Safety and Alignment30 min
14
Capstone: Build a Production LLM App45 min
Prerequisites
- •Basic Python knowledge
- •Understanding of ML concepts helpful but not required
About the Author
PB
Pranay Bathini
Senior Software Engineer @ Booking.com
Building streaming systems at scale by day, writing about them by night. Forever learner, occasional blogger, and firm believer that the best way to understand something is to teach it.
Member of Booking Holdings India AI Committee, working on AI-powered solutions for travel at scale.