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
The Official PyTorch Implementation of "LSGM: Score-based Generative Modeling in Latent Space" (NeurIPS 2021)

The Official PyTorch Implementation of "LSGM: Score-based Generative Modeling in Latent Space" (NeurIPS 2021) Arash Vahdat*   ·   Karsten Kreis*   ·  

NVIDIA Research Projects 238 Jan 02, 2023
A higher performance pytorch implementation of DeepLab V3 Plus(DeepLab v3+)

A Higher Performance Pytorch Implementation of DeepLab V3 Plus Introduction This repo is an (re-)implementation of Encoder-Decoder with Atrous Separab

linhua 326 Nov 22, 2022
Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

Welcome to AirSim AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open

Microsoft 13.8k Jan 05, 2023
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation.

Pretrain-Recsys This is our Tensorflow implementation for our WSDM 2021 paper: Bowen Hao, Jing Zhang, Hongzhi Yin, Cuiping Li, Hong Chen. Pre-Training

30 Nov 14, 2022
Unofficial PyTorch implementation of Attention Free Transformer (AFT) layers by Apple Inc.

aft-pytorch Unofficial PyTorch implementation of Attention Free Transformer's layers by Zhai, et al. [abs, pdf] from Apple Inc. Installation You can i

Rishabh Anand 184 Dec 12, 2022
Learning View Priors for Single-view 3D Reconstruction (CVPR 2019)

Learning View Priors for Single-view 3D Reconstruction (CVPR 2019) This is code for a paper Learning View Priors for Single-view 3D Reconstruction by

Hiroharu Kato 38 Aug 17, 2022
PyTorch implementation for the ICLR 2020 paper "Understanding the Limitations of Variational Mutual Information Estimators"

Smoothed Mutual Information ``Lower Bound'' Estimator PyTorch implementation for the ICLR 2020 paper Understanding the Limitations of Variational Mutu

50 Nov 09, 2022
Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

Streamlit Demo: The Udacity Self-driving Car Image Browser This project demonstrates the Udacity self-driving-car dataset and YOLO object detection in

Streamlit 992 Jan 04, 2023
PolyGlot, a fuzzing framework for language processors

PolyGlot, a fuzzing framework for language processors Build We tested PolyGlot on Ubuntu 18.04. Get the source code: git clone https://github.com/s3te

Software Systems Security Team at Penn State University 79 Dec 27, 2022
A curated list of awesome Deep Learning tutorials, projects and communities.

Awesome Deep Learning Table of Contents Books Courses Videos and Lectures Papers Tutorials Researchers Websites Datasets Conferences Frameworks Tools

Christos 20k Jan 05, 2023
Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.

PyTorch Image Classifier Updates As for many users request, I released a new version of standared pytorch immage classification example at here: http:

JinTian 106 Nov 06, 2022
Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

MyTT Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! to Stock Market Financial Technical Analysis Python

dev 34 Dec 27, 2022
Official code for "EagerMOT: 3D Multi-Object Tracking via Sensor Fusion" [ICRA 2021]

EagerMOT: 3D Multi-Object Tracking via Sensor Fusion Read our ICRA 2021 paper here. Check out the 3 minute video for the quick intro or the full prese

Aleksandr Kim 276 Dec 30, 2022
Automatic Data-Regularized Actor-Critic (Auto-DrAC)

Auto-DrAC: Automatic Data-Regularized Actor-Critic This is a PyTorch implementation of the methods proposed in Automatic Data Augmentation for General

89 Dec 13, 2022
ML model to classify between cats and dogs

Cats-and-dogs-classifier This is my first ML model which can classify between cats and dogs. Here the accuracy is around 75%, however , the accuracy c

Sharath V 4 Aug 20, 2021
WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking

WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking [Paper Link] Abstract In this work, we contribute a new million-scale Un

25 Jan 01, 2023
Homepage of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction [Paper] [PaddlePaddle Implementation] Homepage of paper: Paint Transformer: Fee

442 Dec 16, 2022
Convnext-tf - Unofficial tensorflow keras implementation of ConvNeXt

ConvNeXt Tensorflow This is unofficial tensorflow keras implementation of ConvNe

29 Oct 06, 2022
Fedlearn支持前沿算法研发的Python工具库 | Fedlearn algorithm toolkit for researchers

FedLearn-algo Installation Development Environment Checklist python3 (3.6 or 3.7) is required. To configure and check the development environment is c

89 Nov 14, 2022
DLL: Direct Lidar Localization

DLL: Direct Lidar Localization Summary This package presents DLL, a direct map-based localization technique using 3D LIDAR for its application to aeri

Service Robotics Lab 127 Dec 16, 2022