/nlp-class

A Natural Language Processing course taught by Professor Ghassemi

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

A Hands-on Introduction to Natural Language Processing (NLP)

About this course

This course was created by Prof. Mohammad Ghassemi in Fall of 2020 as part of the CSE 842 class at Michigan State University. The course provides a step-by-step guide to NLP and makes no assumptions that you have a background in the material (NLP or Machine Learning). The content in this repository will teach you:

  1. How to collect and process text data.
  2. How to generate text using language models.
  3. How to classify text using machine learning.
  4. How to use and tune state-of-the-art sequence-to-sequence models, including transformers.
  5. How to process speech signals.

All lectures are hosted on Youtube and can be consumed at your own pace (see links below). At the end of (most) every lecture there is a tutorial + homework assignment that will demonstrate how to perform NLP tasks in Python. The Python Notebooks are available through the links below, and in the Homework folder.

Introduction

NLP Fundamentals and N-gram Language Models

Niave Bayes, Sentiment Classification, Logistic Regression

Vector Semantics, Embeddings, Neural Language Models

Modeling Text as a Sequence

Encoder-Decoder Models, Attention and Transformers

Constituencies, Parsing and Dependency

Speech Processing