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.
Create Chatbots & NLP Apps
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Create Chatbots & NLP Apps
This course is part of AI Techniques, Causal Inference & Business Optimization Specialization
Instructor: Hurix Digital
Included with
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
RAG improves chatbot accuracy by retrieving relevant knowledge before generating replies, reducing hallucinations and boosting context.
Performance-driven development uses metrics like fallback rate, CSAT, and precision/recall to measure, iterate, and improve chatbots.
Choosing TF-IDF vs embeddings shapes system quality: TF-IDF is cheaper, embeddings capture semantics better but cost more compute.
Evaluation-first methodology builds testing and scoring before deployment, so gains are measurable, repeatable, and tied to business value.
Details to know
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Recently updated!
March 2026
Taught in English
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This course is part of the AI Techniques, Causal Inference & Business Optimization Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
Build a chatbot prototype using RAG (retrieval-augmented generation) and measure user satisfaction through SUS survey.
What's included
2 videos2 readings1 assignment1 ungraded lab
Evaluate dialog-flow metrics (fallback rate, turn length) and iterate on intent-matching rules.
What's included
1 video1 reading2 assignments
Apply named-entity recognition to extract key terms from support tickets and quantify precision/recall.
What's included
3 videos2 assignments
Evaluate two vectorization techniques (TF-IDF vs. embeddings) on a text-classification task.
What's included
1 video2 readings2 assignments1 ungraded lab
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