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Machine Learning University: Accelerated Tabular Data Class

This repository contains slides, notebooks, and datasets for the Machine Learning University (MLU) Accelerated Tabular Data 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 tabular data (spreadsheet-like tables), learn about widely used Machine Learning techniques for tabular data, and apply them to real-world problems.

YouTube

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

Course Overview

There are three lectures and one final project for this class. Lecture 1

title	studio lab
Introduction to ML	-
Sample ML Model	-
Model Evaluation
Exploratory Data Analysis
K Nearest Neighbors (KNN)
Final Project

Lecture 2

title	studio lab
Feature Engineering
Tree-based Models
Bagging	-
Hyperparameter Tuning	-
AWS AI/ML Services

Lecture 3

title	studio lab
Optimization	-
Regression Models	-
Boosting	-
Neural Networks	NN MXNet
AutoML

Final Project: Practice working with a "real-world" tabular 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.