Press Mentions and Media

                      




Chatbots: Past, Present and Future

Venue: PyData Seattle 2017


In the past few months, chatbots have made headlines and have become a much-required interface for communication. In this talk, we will discuss three aspects of chatbots:

  1. How chatbots have evolved from Natural Language Processing and Artificial Intelligence In this segment we will answer the following questions: -- What research-areas have led to the evolution of chatbots? -- How important is it for a chatbot to appear human?
  2. Internals and Technologies involved in Chatbots In this segment we will answer the following questions: -- What are the tools and technologies do you need to know about in order to make computers understand human language? -- Which python libraries should one be familiar with to build Language Understanding interfaces?
  3. How to build chatbots yourself and the questions to ask yourself before building one In this segment we will answer the following questions: -- Which API's are available for building chatbots -- Pro/Con's for using API's vs building chatbots from scratch -- Where can one host their chatbots?
Finally we will discuss where chatbots are heading to from here


Voyager

2017

Quoted as an Industry Expert

Flario

2017

Quoted as an Industry Expert

Tech.co

2017

Quoted as an Industry Expert


LEVEL: Data Analytics Bootcamp, 2016

Venue: Northeastern University, Seattle


Level from Northeastern University gives you the skills and real-world experience needed for a career in data analytics. Learn more about the program at www.leveledu.com/seattle.

In this introduction, Instructor Dr. Rutu Mulkar-Mehta and Nick Ducoff, Vice President of New Ventures at Northeastern University, discuss the unique opportunities available through Level.


Natural Language Processing using Python

Venue: PyData Seattle 2015


This tutorial is an introduction to Natural Language Processing using Python, to rapidly build your own NLP module.

We will start with the very basics of NLP - Lemmatization, Stemming, POS tagging, Parsing, Language Models, to the more complex pieces of NLP involving probabilities, statistics and word co-occurrences and finally deep learning approaches to NLP and word vectorization techniques.

As the amount of Unstructured Linguistic Data is increasing each day, it is becoming important to develop tools to analyze this data automatically. In this tutorial I will take you through the basics of linguistic data analytics and then build up to come more complicated pieces of NLP. We will start with basic linguistic techniques - such as Lemmatization, Part of Speech Tagging, Parsing etc, and write some code to implement some these using NLTK. Next, I will talk about how probabilities and statistics are used with Linguistic Data Processing to develop Language Models, and finally we will talk about more complicated techniques such as Deep Learning. In particular we will talk about Word2Vec, its strengths, its weaknesses and how to use it.


Redefine, Rebrand, Re-launch!

Venue: ACT-W Conference, 2015

Dr. Rutu Mulkar-Mehta speaks at 1h 22mins: