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.
Watch all Tabular Data class video recordings in this YouTube playlist from our YouTube channel.
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 |
| 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.
Interested in visual, interactive explanations of core machine learning concepts? Check out our MLU-Explain articles to learn at your own pace!
If you would like to contribute to the project, see CONTRIBUTING for more information.
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.