Text Analysis in Python

Schedule

	Setup	Download files required for the lesson
00:00	1. Introduction to Natural Language Processing	What is Natural Language Processing? What tasks can be done by Natural Language Processing? What does a workflow for an NLP project look?
00:35	2. APIs	How do I know what data I can use for my corpus? How can I use an API to acquire data?
01:15	3. Preparing and Preprocessing Your Data	How can I prepare data for NLP? What are tokenization, casing and lemmatization?
01:35	4. Vector Space and Distance	How can we model documents effectively? How can we measure similarity between documents? What’s the difference between cosine similarity and distance?
02:15	5. Document Embeddings and TF-IDF	What is a document embedding? What is TF-IDF?
02:45	6. Latent Semantic Analysis	What is topic modeling? What is Latent Semantic Analysis (LSA)?
03:15	7. Intro to Word Embeddings	How can we extract vector representations of individual words rather than documents? What sort of research questions can be answered with word embedding models?
04:00	8. The Word2Vec Algorithm	How does the Word2Vec model produce meaningful word embeddings? How is a Word2Vec model trained?
04:45	9. Training Word2Vec	How can we train a Word2Vec model? When is it beneficial to train a Word2Vec model on a specific dataset?
05:50	10. LLMs and BERT Overview	What is a large language model? What is BERT? How does attention work?
06:30	11. Finetuning LLMs	How can I fine-tune preexisting LLMs for my own research? How do I pick the right data format? How do I create my own labels? How do I put my data into a model for finetuning? How do I evaluate success at my task?
08:30	12. Ethics and Text Analysis	Is text analysis artificial intelligence? How can training data influence results? What are the risk zones to consider when using text analysis for research?
09:10	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.