Machine Learning University: Accelerated Natural Language Processing Class

Last update: Jan 01, 2023

Overview

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	Lecture 2	Lecture 3
Introduction to ML	Tree-based Models	Neural Networks
Intro to NLP and Text Processing	Regression Models	Word Embeddings
Bag of Words (BoW)	Optimization-Regularization	Recurrent Neural Networks (RNN)
K Nearest Neighbors (KNN)	Hyperparameter Tuning	Transformers
	AWS AI/ML Services

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.

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.

Machine Learning University: Accelerated Natural Language Processing Class

Related tags

Overview

Machine Learning University: Accelerated Natural Language Processing Class

YouTube

Course Overview

Contribute

License

Owner

AWS Samples

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

Apple-voice-recognition - Machine Learning

K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

Machine learning that just works, for effortless production applications

A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile

pywFM is a Python wrapper for Steffen Rendle's factorization machines library libFM

A Python Module That Uses ANN To Predict A Stocks Price And Also Provides Accurate Technical Analysis With Many High Potential Implementations!

A Collection of Conference & School Notes in Machine Learning 🦄📝🎉

Primitives for machine learning and data science.

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Diabetes Prediction with Logistic Regression

BigDL: Distributed Deep Learning Framework for Apache Spark

Formulae is a Python library that implements Wilkinson's formulas for mixed-effects models.

Solve automatic numerical differentiation problems in one or more variables.

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

Fit interpretable models. Explain blackbox machine learning.

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Iris-Heroku - Putting a Machine Learning Model into Production with Flask and Heroku

Avocado hass time series vs predict price

Python implementation of Weng-Lin Bayesian ranking, a better, license-free alternative to TrueSkill