Program Overview
Dive deep into Large Language Models and learn how to operate them efficiently. This specialization covers everything from foundational LLM concepts to fine-tuning, deployment, and best practices in LLMOps.
Learners will gain hands-on experience fine-tuning models, deploying them as APIs, monitoring performance, and implementing operational best practices for LLMs in real-world applications.
What you’ll learn
- Understand LLMs — Grasp transformers, tokenization, embeddings, and LLM mechanics
- Fine-Tune & Deploy — Implement domain-specific LLM solutions and deploy APIs
- LLMOps Best Practices — Monitor performance, optimize cost, ensure security, and automate workflows
- Career Ready Skills — Prepare for AI engineering, ML ops, and data-driven roles using LLMs
Skills you’ll learn
- Large Language Models
- LLMOps
- Fine-Tuning
- Deployment
- Automation
- AI Ethics
Tools you’ll learn
- Python 3
- Hugging Face Transformers
- PyTorch
- Docker
- FastAPI
- Weights & Biases
Comprehensive Curriculum
3 modules, 24+ lessons, 12 weeks of LLM-focused hands-on learning
Key Topics
- Introduction to Large Language Models
- History and evolution of LLMs
- Transformer architecture overview
- Tokenization and embeddings
- Ethical considerations in AI
- LLM performance metrics
Hands-on Projects
- Explore pre-trained LLMs using Python
- Tokenize text and visualize embeddings
