The Foundations of Machine Learning and Generative AI course equips participants with the foundational knowledge to explore more advanced topics such as Retrieval-Augmented Generation (RAG), improving agents for more complex workflows, and deploying scalable applications powered by LLMs. By understanding the basics of model fine-tuning and transformer mechanics, participants will be prepared to tackle next-level challenges like advanced LLM optimization, hybrid models, and efficient application deployment in cloud or local environments. After completing this course, participants will have the skills to design functional prototypes, understand the principles of transfer learning, and leverage LLM APIs for advanced integrations.
Prerequsites:
- Basic Python knowledge
- Some experienice with NumPy and Pandas
Purpose
| Develop skills to design functional prototypes, understand the principles of transfer learning, and leverage LLM APIs for advanced integrations. |
Audience
| Engineers and Developers looking to advance their skills and knowledge of machine learning, deep learning, and large language models (LLMs). |
Role
| Software Developers | Data Scientists |
Skill level
| Intermediate |
Style
| Lecture | Hands-on Activities | Use Cases | Labs |
Duration
| 3 days |
Related technologies
| Gen AI | ML } Python | NumPy | Hugging Face | TensorFlow | Keras |
Course objectives
- Understand Core Machine Learning Concepts
- Perform Practical Natural Language Processing (NLP)
- Develop Foundational Deep Learning Skills
- Explore Transformers and Prompt Engineering
- Implement Transfer Learning with Pre-Trained Models
- Create and Integrate a Chatbot Using LLMs
- Build Confidence in Applying LLMs and ML Tools