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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.
12 lessons6 hoursBeginner
Course in Development
This course is currently being created. Follow me to get notified when it launches.
What You'll Learn
How transformer architecture enables language understanding
Tokenization strategies and their impact on model performance
Prompt engineering techniques for reliable outputs
When and how to fine-tune models for your use case
Evaluation metrics for language models
Cost optimization strategies for LLM applications
Course Syllabus
1
Introduction to Language Models25 min
2
The Transformer Architecture40 min
3
Attention Mechanisms Explained35 min
4
Tokenization Deep Dive30 min
5
Prompt Engineering Fundamentals45 min
6
Advanced Prompting Techniques40 min
7
Fine-tuning Strategies50 min
8
LoRA and Parameter-Efficient Fine-tuning35 min
9
Evaluation and Benchmarking30 min
10
Cost Optimization25 min
11
Safety and Alignment30 min
12
Putting It All Together35 min
Prerequisites
- •Basic Python knowledge
- •Understanding of ML concepts helpful but not required
About the Author
PB
Pranay Bathini
Senior Software Engineer @ Booking.com
Leading development of critical infrastructure and mentoring team members in building scalable distributed systems.
Member of Booking Holdings India AI Committee. Designed and implemented guest verification backend for ID verification at scale.