Ai+ Training

This hands-on course is organized into four lessons. In lesson one, we will provide an introduction to NLP, reviewing its evolution over the past 70 years. We will explain why NLP matters and how it powers many of the most popular applications we use every day. We will also perform basic NLP tasks using one of the most popular open-source NLP libraries today: spaCy. In lesson two, we will introduce two more popular open-source NLP libraries (fast.ai and Hugging Face) and perform state-of-the-art NLP. We will develop a sentiment analysis model for IMDb movie reviews. We will also cover modern NLP concepts such as attention mechanisms, transformers, pretrained language models, transfer learning, and fine-tuning. In lesson three, we will retrace how NLP advanced over the last decade and experienced its breakout moment in 2018. Since 2018, NLP has soared in popularity among companies and has become a mainstream topic of interest. After we cover the theory, we will discuss modern NLP tasks such as sequence classification, question answering, language modeling, text generation, named entity recognition, summarization, and translation. In lesson four, we will put this theory to practice and develop our own named entity recognition and text classification models using spaCy, including annotating our data using an annotation platform called Prodigy. We will draw on what we’ve learned to perform transfer learning and fine-tuning, and we will compare the fine-tuned model’s performance against an out-of-the-box named entity recognition model. By the end of this course, you should have a good understanding of the fundamental concepts in NLP, both in theory and in practice.