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.
There are three lectures and one final project for this class.
|Lecture 1||Lecture 2||Lecture 3|
|Introduction to ML||Feature Engineering||Optimization|
|Sample ML Model||Tree-based Models||Regression Models|
|Exploratory Data Analysis||Hyperparameter Tuning||Neural Networks|
|K Nearest Neighbors (KNN)||AWS AI/ML Services||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.