Machine Learning University: Accelerated Natural Language Processing Class

This repository contains slides, notebooks and datasets for the Machine Learning University (MLU) Accelerated Natural Language Processing class. Our mission is to make Machine Learning accessible to everyone. We have courses available across many topics of machine learning and believe knowledge of ML can be a key enabler for success. This class is designed to help you get started with Natural Language Processing (NLP), learn widely used techniques and apply them on real-world problems.

YouTube

Watch all NLP class video recordings in this YouTube playlist from our YouTube channel.

Course Overview

There are three lectures and one final project in this class. Course overview is below.

Lecture 1

title	studio lab
Introduction to ML	-
Intro to NLP and Text Processing
Bag of Words (BoW)
K Nearest Neighbors (KNN)
Final Project

Lecture 2

title	studio lab
Tree-based Models
Regression Models	Linear Regression Logistic Regression
Optimization-Regularization	-
Hyperparameter Tuning	-
Final Project

Lecture 3

title	studio lab
Neural Networks
Word Embeddings
Recurrent Neural Networks (RNN)
Final Project

Final Project: Practice working with a "real-world" NLP dataset for the final project. Final project dataset is in the data/final_project folder. For more details on the final project, check out this notebook.

Interactives/Visuals

Interested in visual, interactive explanations of core machine learning concepts? Check out our MLU-Explain articles to learn at your own pace!

Contribute

If you would like to contribute to the project, see CONTRIBUTING for more information.

License

The license for this repository depends on the section. Data set for the course is being provided to you by permission of Amazon and is subject to the terms of the Amazon License and Access. You are expressly prohibited from copying, modifying, selling, exporting or using this data set in any way other than for the purpose of completing this course. The lecture slides are released under the CC-BY-SA-4.0 License. The code examples are released under the MIT-0 License. See each section's LICENSE file for details.