Understanding, Testing, and Fine-tuning AI Models with the HuggingFace Library is a comprehensive course designed to guide you from the foundational concepts of machine learning all the way to advanced techniques for building your own Large Language Models (LLMs). Perfect for both newcomers and experienced engineers, this course ensures you gain hands-on expertise in every step of the machine learning pipeline—ranging from basic principles to sophisticated model testing, fine-tuning, and deployment.

What You’ll Learn

1. Introduction to Machine Learning

Lay a strong foundation by exploring key ML concepts and essential terminology.

2. Working with Natural Language Processing (NLP) Libraries

Discover how to process, analyze, and derive insights from textual data using popular NLP tools.

3. In-Depth Understanding of the Transformers Library

Dive deep into HuggingFace’s Transformers, the gold standard for building state-of-the-art NLP and LLM solutions.

4. Various Ways to Work with Large Language Models (LLMs)

Learn multiple methods to interact with and utilize LLMs for diverse real-world applications.

5. Functional Testing of AI Models

Ensure your models work reliably under different scenarios by applying systematic testing strategies.

6. Bias and Fairness Testing

Master techniques to detect and mitigate unintended bias, fostering ethical and equitable AI practices.

7. Evaluating AI Models

Measure performance using robust metrics and refine your models for optimal results.

8. Working with AI Agents

Understand how to build, configure, and integrate intelligent agents into your workflows.

9. Fine-tuning and Training AI Models

Customize pre-trained models or build your own from scratch, tailoring them to your specific project requirements.

By the end of this course, you’ll be equipped with the knowledge and practical experience to confidently develop, test, and optimize your own Transformer-based models and LLMs, setting you on an exciting path in the rapidly evolving world of AI.

Who this course is for:

• QA
• Dev
• SRE
• Data scientists
• Anyone who want to learn AI and upskill