Create Chatbots & NLP Apps
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.
Watch on Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: RAG Basics
View skill →Related AI Lessons
⚡
⚡
⚡
⚡
The Future of RAG: Dead, Evolving… or Becoming the Brain of AI?
Medium · Machine Learning
Smart Routing, Transfer Family Ingestion, and Voice Chat — Permission-Aware RAG v4.2
Dev.to · Yoshiki Fujiwara(藤原 善基)@AWS Community Builder
Most Companies Doing GenAI Are Really Just Doing RAG: RAGOps Explained for analysts
Medium · RAG
RAG - Sliding Window, Token Based Chunking and PDF Chunking Packages
Dev.to AI
🎓
Tutor Explanation
DeepCamp AI