# Chatbots to Drive Student Engagement in Higher Education

Whether you are ready for chatbots or not, they have been the future we live in now. Chatbots are not robots, but they are supposed to mimic humans. They are a piece of software that you chat with to get things done or be entertained. You have probably used Apple’s Siri, Google Assistant or Microsoft’s Cortana. But chatbots are more than just the built-in personal assistants. They are everywhere now; from ordering pizza to checking flight status

I have run an experiment to use chatbots in higher education in order to drive student engagement beyond the classroom experience. In this post, I am going to share my experience in developing a chatbot teaching assistant, called Koko. Continue reading

# An Introduction to N-grams: What Are They and Why Do We Need Them?

In this post I am going to talk about N-grams, a concept found in Natural Language Processing ( aka NLP). First of all, let’s see what the term ‘N-gram’ means. Turns out that is the simplest bit, an N-gram is simply a sequence of N words. For instance, let us take a look at the following examples.

1. San Francisco (is a 2-gram)
2. The Three Musketeers (is a 3-gram)
3. She stood up slowly (is a 4-gram)

Now which of these three N-grams have you seen quite frequently? Probably, “San Francisco” and “The Three Musketeers”. On the other hand, you might not have seen “She stood up slowly” that frequently. Basically, “She stood up slowly” is an example of an N-gram that does not occur as often in sentences as Examples 1 and 2.

Now if we assign a probability to the occurrence of an N-gram or the probability of a word occurring next in a sequence of words, it can be very useful. Why? Continue reading

# Build your own Natural Language Processing based Intelligent Assistant using Python, It’s easy!

Before we begin, let us talk about how Mike (a fictional character) spends a typical morning. Mike begins his day by searching for breakfast recipes on Google Now (https://en.wikipedia.org/wiki/Google_Now). After a filling breakfast, Mike starts getting ready for work. He asks Siri (http://www.apple.com/in/ios/siri/) to tell him the weather and traffic conditions for his drive to work. Finally, as Mike gets ready to leave the house, he asks Alexa (https://en.wikipedia.org/wiki/Amazon_Alexa) to dim the lights and thermostat. It is not even 10 a.m. yet, but Mike like many of us has already used three intelligent personal assistant applications using Natural Language Processing (NLP). We will unravel the mysteries of building intelligent personal assistants with a simple example to build such an assistant quite easily using NLP.

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