Create Chatbots and NLP Applications

Ready to transform customer interactions through intelligent conversation? This Short Course was created to help data analysts and professionals accomplish the development of sophisticated chatbot applications with natural language processing capabilities. By completing this course, you'll be able to implement retrieval-augmented generation systems, optimize conversational flows, extract meaningful insights from unstructured text, and make data-driven decisions about text representation methods. By the end of this course, you will be able to: Build a chatbot prototype using RAG (retrieval-augmented generation) and measure user satisfaction through SUS survey Evaluate dialog-flow metrics (fallback rate, turn length) and iterate on intent-matching rules Apply named-entity recognition to extract key terms from support tickets and quantify precision/recall Evaluate two vectorization techniques (TF-IDF vs. embeddings) on a text-classification task This course is unique because it combines hands-on chatbot development with rigorous evaluation methodologies, ensuring your AI solutions deliver measurable business value. To be successful in this project, you should have a background in Python programming and basic machine learning concepts

• Implement retrieval-augmented generation to enhance chatbot accuracy
• Use performance metrics to measure and improve chatbots
• Choose appropriate text representation methods for system quality
• Build evaluation-first testing and scoring methodologies

• Basic familiarity with relevant AI and chatbot terminology
• Readiness for applied exercises and practice