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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Chapter 8. Developing Chatbots

The year 2017 was all about chatbots, and that continues in 2018. Chatbots are not new at all. The concept of chatbots has been around since the 1970s. Sometimes, a chatbot application is also referred to as a question-answering system. This is a more specific technical term for a chatbot. Let's take a step into history. Lunar was the first rule-based question-answering system. Using this system, geologists could ask questions regarding the moon rock from the Apollo missions. In order to improvise the rule-based system that was used in the Apollo mission, we had to find out a way to encode pattern-based question and answers. For this purpose, Artificial Intelligence Markup Language was used, also called AIML. This helps the programmer code less lines of code in order to achieve the same result that we generated by using a hardcoded pattern-based system. With recent advances in the field of Machine Learning (ML), we can build a chatbot without hardcoded responses...

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