Create Chatbots & NLP Apps

Overview

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.

Syllabus

• Module 1: RAG Chatbot Development - Foundation
Build a chatbot prototype using RAG (retrieval-augmented generation) and measure user satisfaction through SUS survey.
• Module 2: Dialog Flow Optimization - Core Application
Evaluate dialog-flow metrics (fallback rate, turn length) and iterate on intent-matching rules.
• Module 3: Named Entity Recognition - Integration
Apply named-entity recognition to extract key terms from support tickets and quantify precision/recall.
• Module 4: Text Vectorization Evaluation - Assessment
Evaluate two vectorization techniques (TF-IDF vs. embeddings) on a text-classification task.

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Taught by

Hurix Digital

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Subjects

Artificial Intelligence