Many domain experts encounter significant challenges in leveraging machine learning due to the technical complexity of model selection and development. This thesis presents LLM-PQA, a system that enables natural language interaction with machine learning functionalities through large language models. The system combines LLM-based query interpretation with vector search techniques for resource matching and automated machine learning tools for model development. We provide a benchmark dataset for evaluating natural language-driven machine learning systems and conduct experiments examining feature extraction accuracy and embedding technique effectiveness, along with a comprehensive analysis of AutoML tool integration capabilities. Using our benchmark dataset, the evaluation demonstrates that one-shot prompting with LLMs achieves reliable feature extraction, while normalized vector embeddings enable precise resource matching. Analysis of various AutoML tools reveals integration strategies that maintain both technical capability and user accessibility. These findings advance the development of natural language interfaces for machine learning systems while providing insights into effective integration patterns for automated machine learning tools.