COURSE OVERVIEW

This hands-on, 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 AI Natural Language Processing. It covers the importance of natural language processing (NLP) and the fundamentals of text processing; the regular expressions (Regex) for text processing, word embeddings, sentence parsing and dependency analysis; the bag of words (BoW) and TF-IDF representations, N-grams and feature extraction; the sentiment analysis with machine learning, named entity recognition (NER), text categorization, word embeddings and contextual representation; and the deep learning for NLP, recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. 

 

During this interactive course, participants will learn the transformer models in NLP, training a text generation model with GPT and text summarization techniques; question answering systems with NLP and machine translation with NLP; the machine translation with NLP, fake news detection and text moderation and deploying NLP models in production; running NLP models on mobile devices, optimizing NLP models for edge AI and using TensorFlow lite for on-device NLP; and addressing bias in NLP models, privacy concerns in AI-driven text processing including fairness and transparency in AI-generated text.