FeaturedUdemy
Introduction to LLMs in Python
4.8(1,781)34k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This hands-on intermediate course demystifies Large Language Models (LLMs) and transformer architectures in Python. Using pre-trained models from Hugging Face, it equips developers with the practical skills needed to fine-tune, evaluate, and deploy generative AI for language generation, translation, and custom NLP tasks.
Who It's For
Python developers, data scientists, and AI practitioners looking to build, fine-tune, and evaluate state-of-the-art Large Language Models.
Key Takeaways
- Transformer Architectures: Understand core transformer mechanisms and differentiate between modern LLM models.
- Hugging Face Ecosystem: Leverage pre-trained language models and datasets for custom downstream tasks.
- Fine-Tuning & Evaluation: Fine-tune LLMs and evaluate model performance using specialized evaluation metrics.
- Ethics & Bias: Identify ethical concerns, address bias, and build responsible real-world LLM applications.
About this course
Uncover What's Behind the Large Language Models Hype
Large Language Models (LLMs) have become pivotal tools driving some of the most stunning advancements and applications in today's AI landscape. This hands-on course will equip you with the practical knowledge and skills needed to understand, build, and harness the power of LLMs for solving complex language tasks such as translation, language generation, and more.
Discover LLM Architecture and Leverage Pre-Trained Models
Through interactive coding exercises, you'll discover different transformer architectures and how to identify them. You'll explore leveraging pre-trained language models and datasets from Hugging Face for fine-tuning and evaluating your model using advanced metrics that fit LLMs. Finally, you'll find out more about ethical and bias concerns relevant to LLMs and ways to identify these. By the end of this course, you will be able to build LLMs, fine-tune, and evaluate them using specialized metrics while understanding the key challenges and ethical considerations of enabling real-world LLM applications.
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