Chatbot Building with Rasa
What you'll learn
- Understanding concepts of building chatbots with Rasa NLU, Rasa Core, DialogFlaw & Wit•ai
- Building chatbots for Facebook Messenger
- Buiding a chatbot that answers FAQs
- Deploying your chatbot in Heroku application platform
- Its necessary to have basic programming knowledge of Python
Do you want to create a talking chatbot that interacts with your visitors? In this tutorial, you will learn how to create Python chatbots using Rasa NLU and Rasa Core. They provide several Natural Language processing functions that parse user input and match it to the right response. Integrating NLP into your bot can be difficult, but with Rasa, it is much easier to create a Facebook Messenger bot or a website chatbot.
Rasa is a powerful open-source machine learning framework for developers to create contextual chatbots and expand bots beyond answering simple questions. In this course, you will study both Rasa NLU and Rasa Core.
Rasa NLU is an open-source natural language processing tool for intent classification and entity extraction in chatbots. You can think of it as a set of high-level APIs for building your own language parser using existing NLP and ML libraries. Among the main reasons for using open-source NLU are: 1) you don’t have to hand over all your chatbot training data to Google, Microsoft, Amazon, or Facebook; 2) Machine Learning is not one-size-fits-all. You can tweak and customize Python chatbot models for your training data; and 3) Rasa NLU runs wherever you want, so you don’t have to make an extra network request for every chatbot message that comes in.
Rasa Core leverages developers’ existing domain knowledge to help them bootstrap from zero training data, and adopts an interactive learning approach. With Rasa Core, you manually specify all the things your bot can say and do. We call these actions. One action might be to greet the user, another might be to call an API, or query a database. Then you train a probabilistic model to predict which action your Python chatbot should take given the history of a chatbot conversation.
This Python chatbot course will help you:
Build chatbots with Python using Rasa NLU & Rasa Core
Understand intents and entities.
Build a Facebook Messenger bot.
Deploy chatbots on cloud platforms such as Heroku.
Who this course is for:
- Software Python developers looking to build chatbots for their websites and mobile apps
- Developers of Facebook looking to build Massenger chatbots
- Development professionals and students looking to learn how to use Rasa NLU, Rasa Core, DialogFlow and Wit-AI to build chatbots.
GoTrained is an e-learning academy aiming at creating useful content in different languages and it concentrates on technology and management.
We adopt a special approach for selecting content we provide; we mainly focus on skills that are frequently requested by clients and jobs while there are only few videos that cover them. We also try to build video series to cover not only the basics, but also the advanced areas.
AI Enthusiast and a Software Developer with over 6+ years of development experience using multiple open source technologies. Presently, I'm freelancing Computer Vision and Chatbot Development projects.
I started my Freelance career back in 2011, have been doing multiple projects of multiple types. Since, I have a Bachelors Degree in Marine Engineering, I used to do Freelance CAD/CAM projects initially. I have always been a good self learner and still learn different things out of curiosity. When I learned enough web development, I started doing Web Development projects and used to provide Wordpress Development and SEO services back in 2012 - 2016. Then these very interesting fields of Data Science, Machine Learning and Big Data really inspired me and I started learning things about them on difference platforms like Coursera, Udemy and Udacity. I have around 30+ certificates of courses and specializations from Coursera and still doing some courses along with my freelance work. I have done a Full Stack Web Development specialization from Hong Kong University of Science and Technology and a few other courses from John Hopkins, University of California San Diego; in Big Data, Rails Development, Data Science with R and etc.
Presently I have been involved in following projects:
1) Detect area of water present inside an image of field/pond/site with Deep Learning and Computer Vision Techniques
2) Detect number of people and machinery available in a field/site with Deep Learning
3) Fashion apparel detection with Deep Learning
4) Developing an application to bulk upload training examples for a Dialogflow intent
5) Conversational application development with Python using Rasa
I do all the projects myself, so I do each and every task myself within any kind of development, i.e. from selecting the suitable model to training the model, preparing training data, serving the application with Flask, Serverless or anything, developing UI with Vue or React and etc.