Author name: Divsgo Technologies

A chatbot is a piece of AI-based software that can converse with humans in their own language. These chatbots often connect with humans through audio or written means, and they can easily mimic human languages to speak with them in a human-like manner. The Rule-based approach teaches a chatbot to answer queries based on a set of pre-determined rules that it was taught when it was first created. These predetermined rules can be simple or complex. Self-learning bots, as the name implies, are bots that can train on their own. These take advantage of cutting-edge technology like Artificial Intelligence and Machine Learning to learn from examples and behaviors. There are several ways to create a chatbot in Python, but the most common one is to use a library called ChatterBot. Here is an example of how you can use ChatterBot to create a simple chatbot in Python: This will create a chatbot that uses a corpus of pre-defined greetings and conversation prompts to generate responses. You can train the chatbot with your own data by providing a list of strings to the trainer.train method. In this article, you will learn How to Make a Chatbot in Python Step By Step. Prepare the Dependencies Import Classes Create and Train the Chatbot Communicate with the Python Chatbot Train your Python Chatbot with a Corpus of Data. How to Make a Chatbot in Python? In the past few years, chatbots in Python have become wildly popular in the tech and business sectors. These intelligent bots are so adept at imitating natural human languages and conversing with humans, that companies across various industrial sectors are adopting them. From e-commerce firms to healthcare institutions, everyone seems to be leveraging this nifty tool to drive business benefits. In this article, we will learn about chatbots using Python and how to make chatbots in python. What is a Chatbot? A chatbot is an AI-based software designed to interact with humans in their natural languages. These chatbots usually converse via auditory or textual methods, and they can effortlessly mimic human languages to communicate with human beings in a human-like manner. A chatbot is arguably one of the best applications of natural language processing. Chatbots can be categorized into two primary variants – Rule-Based and Self-learning. The Rule-based approach trains a chatbot to answer questions based on a set of pre-determined rules on which it was initially trained. These set rules can either be very simple or very complex. While rule-based chatbots can handle simple queries quite well, they usually fail to process more complicated queries/requests. As the name suggests, self-learning bots are chatbots that can learn on their own. These leverage advanced technologies like Artificial Intelligence and Machine Learning to train themselves from instances and behaviors. Naturally, these chatbots are much smarter than rule-based bots. Self-learning bots can be further divided into two categories – Retrieval Based or Generative. 1. Retrieval-based Chatbots A retrieval-based chatbot is one that functions on predefined input patterns and set responses. Once the question/pattern is entered, the chatbot uses a heuristic approach to deliver the appropriate response. The retrieval-based model is extensively used to design goal-oriented chatbots with customized features like the flow and tone of the bot to enhance the customer experience. 2. Generative Chatbots Unlike retrieval-based chatbots, generative chatbots are not based on predefined responses – they leverage seq2seq neural networks. This is based on the concept of machine translation where the source code is translated from one language to another language. In the seq2seq approach, the input is transformed into an output. The first chatbot dates back to 1966 when Joseph Weizenbaum created ELIZA which could imitate the language of a psychotherapist in only 200 lines of code. However, thanks to the rapid advancement of technology, we’ve come a long way from scripted chatbots to chatbots in python today.  Chatbot in Today’s Generation Today, we have smart AI-powered Chatbots that use natural language processing (NLP) to understand human commands (text and voice) and learn from experience. Chatbots have become a staple customer interaction tool for companies and brands that have an active online presence (website and social network platforms). Chatbots using python are a nifty tool since they facilitate instant messaging between the brand and the customer. Think about Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana. Aren’t these just wonderful? Aren’t you already curious to learn how to make a chatbot in Python?  Source Essentially, the chatbot using Python is programmed to take in the information you provide to it and then analyze it with the help of complex AI algorithms, and provide you with either a written or verbal response. Since these bots can learn from behavior and experiences, they can respond to a wide range of queries and commands.  Source  Although chatbot in python has already begun to dominate the tech scene at present, Gartner predicts that by 2020, chatbots will handle nearly 85% of customer-brand interactions. In light of the increasing popularity and adoption of chatbots in the industry, you can increase your market value by learning how to make a chatbot in Python – one of the most extensively used programming languages in the world. Today, we will teach you how to make a simple chatbot in Python using the ChatterBot Python library. ChatterBot Library ChatterBot is a Python library that is designed to deliver automated responses to user inputs. It makes use of a combination of ML algorithms to generate many different types of responses. This feature allows developers to build chatbots using python that can converse with humans and deliver appropriate and relevant responses. Not just that, the ML algorithms help the bot to improve its performance with experience.  Another excellent feature of ChatterBot is its language independence. The library is designed in a way that makes it possible to train your bot in multiple programming languages. How does ChatterBot function? When a user enters a specific input in the chatbot (developed on ChatterBot), the bot saves the input along with the response, for future use. This data (of collected experiences) allows the chatbot to generate automated