COURSE OVERVIEW

This practical and highly-interactive course includes real-life case studies and exercises where participants will be engaged in a series of interactive small groups and class workshops. 

This course is designed to provide participants with a detailed and up-to-date overview of How to Build Your Own Chatbot using Python. It covers the types of chatbots and its applications in various industries; setting up python for chatbot development and the basics of natural language processing (NLP); the rule-based chatbot logic, using if-else conditions for chatbot responses and creating a simple chatbot with python dictionaries; how chatbots learn from data, choosing the right dataset for training and chatbot response generation; and the sentence structure, word embeddings, vectorizing text for machine learning and context understanding in chatbots. 

 

Further, the course will also discuss the building of chatbot using NLTK and spaCy, implementing bag-of-words (BoW) & TF-IDF and intent recognition for chatbots; collecting and preparing chatbot training data, cleaning and preprocessing conversational datasets; using prebuilt datasets for chatbot training and annotating datasets for intent recognition; the traditional ML and deep learning; and implementing recurrent neural networks (RNNs) for chatbots. 

During this interactive course, participants will learn the use of LSTM and GRU for context-aware chatbots, transformer-based chatbots and BERT for question answering; choosing the right model for chatbot, using hugging face transformers for chatbot training and deploying a chatbot using a pretrained transformer model; adding context and memory to chatbots, speech-to-text and voice-enabled chatbots and integrating chatbots with APIs and databases; deploying chatbots on messaging platforms and security and ethical considerations in chatbots; and the advanced NLP techniques for better responses, multi-language chatbot development and AI-powered personalization in chatbots.