다음에 대한 학습 방향 Technical Professionals
Generative AI for Data Science
This starter learning path equips data scientists with practical skills in leveraging ChatGPT for data analysis, machine learning, and advanced data wrangling. It covers foundational and advanced concepts in generative AI, deep learning, LLM-powered application development, fine-tuning with Hugging Face, RAG, vector embeddings, multi-agent systems, and ethical considerations in AI and data science.
Learning objectives:
- Apply ChatGPT and generative AI tools to accelerate data exploration, cleaning, feature engineering, and machine learning workflows in Python.
- Develop, fine-tune, and deploy LLM-powered applications using frameworks such as Hugging Face, LangChain, and OpenAI APIs.
- Master advanced techniques in deep learning, including neural networks, transformers, and multimodal LLMs for diverse data science tasks.
- Implement Retrieval-Augmented Generation (RAG) systems and utilize vector embeddings and databases to enhance LLM application performance.
- Evaluate and address ethical challenges in data science and AI, including data stewardship, algorithmic bias, and responsible use of generative models.
Target audience:
This path is designed for data scientists, machine learning engineers, and analytics professionals seeking to integrate generative AI and LLMs into their workflows. It is also suitable for Python developers and technical practitioners interested in building, fine-tuning, and deploying advanced AI solutions with a strong ethical foundation.