Aws-machine-learning-university-accelerated-tab - Machine Learning University: Accelerated Tabular Data Class

Overview

logo

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.

Playlist

Course Overview

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
Model Evaluation Bagging Boosting
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.

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.

Owner
AWS Samples
AWS Samples
Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

66 Dec 15, 2022
Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

Pytorch Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic [Paper] [Colab is coming soon] Approach Example Usage To r

170 Jan 03, 2023
Official PyTorch Implementation of "Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs". NeurIPS 2020.

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs This repository is the implementation of SELAR. Dasol Hwang* , Jinyoung Pa

MLV Lab (Machine Learning and Vision Lab at Korea University) 48 Nov 09, 2022
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields.

This repository contains the code release for Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. This implementation is written in JAX, and is a fork of Google's JaxNeRF

Google 625 Dec 30, 2022
Efficient Training of Audio Transformers with Patchout

PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa

165 Dec 26, 2022
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

111 Dec 29, 2022
Citation Intent Classification in scientific papers using the Scicite dataset an Pytorch

Citation Intent Classification Table of Contents About the Project Built With Installation Usage Acknowledgments About The Project Citation Intent Cla

Federico Nocentini 4 Mar 04, 2022
Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection Main requirements torch = 1.0 torchvision = 0.2.0 Python 3 Environm

15 Apr 04, 2022
The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.

AICITY2021_Track2_DMT The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop. Introduction

Hao Luo 91 Dec 21, 2022
Self-Supervised Contrastive Learning of Music Spectrograms

Self-Supervised Music Analysis Self-Supervised Contrastive Learning of Music Spectrograms Dataset Songs on the Billboard Year End Hot 100 were collect

27 Dec 10, 2022
Using Python to Play Cyberpunk 2077

CyberPython 2077 Using Python to Play Cyberpunk 2077 This repo will contain code from the Cyberpython 2077 video series on Youtube (youtube.

Harrison 118 Oct 18, 2022
Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning

Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning Reference Abeßer, J. & Müller, M. Towards Audio Domain Adapt

Jakob Abeßer 2 Jul 06, 2022
A GridMixup augmentation, inspired by GridMask and CutMix

GridMixup A GridMixup augmentation, inspired by GridMask and CutMix Easy install pip install git+https://github.com/IlyaDobrynin/GridMixup.git Overvie

IlyaDo 42 Dec 28, 2022
An Intelligent Self-driving Truck System For Highway Transportation

Inceptio Intelligent Truck System An Intelligent Self-driving Truck System For Highway Transportation Note The code is still in development. OS requir

InceptioResearch 11 Jul 13, 2022
[BMVC'21] Official PyTorch Implementation of Grounded Situation Recognition with Transformers

Grounded Situation Recognition with Transformers Paper | Model Checkpoint This is the official PyTorch implementation of Grounded Situation Recognitio

Junhyeong Cho 18 Jul 19, 2022
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

AI Wizards for Software Management (AWSM) Research Group 14 Nov 13, 2022
Lava-DL, but with PyTorch-Lightning flavour

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

Sami BARCHID 4 Oct 31, 2022
Compartmental epidemic model to assess undocumented infections: applications to SARS-CoV-2 epidemics in Brazil - Datasets and Codes

Compartmental epidemic model to assess undocumented infections: applications to SARS-CoV-2 epidemics in Brazil - Datasets and Codes The codes for simu

1 Jan 12, 2022
A Python library for differentiable optimal control on accelerators.

A Python library for differentiable optimal control on accelerators.

Google 80 Dec 21, 2022
Embracing Single Stride 3D Object Detector with Sparse Transformer

SST: Single-stride Sparse Transformer This is the official implementation of paper: Embracing Single Stride 3D Object Detector with Sparse Transformer

TuSimple 385 Dec 28, 2022