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

AWS Samples

Last update: Dec 23, 2022

Related tags

Deep Learning python machine-learning deep-learning mxnet sklearn tabular-data gluon

Overview

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	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.

Comments

Added Open Studio Lab button
Issue #, if available:

Description of changes:

Added Open Studio Lab button to each notebook at README.md

Added Open Studio Lab button to each MLU courses at MLU-MAIN.ipynb

I made sure that each button works.

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opened by wildgeece96 2
Updated AutoGluon and installation procedures

Description of changes: Updated AutoGluon notebook to be compatible with the most recent version APIs.

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opened by gradientsky 2

Problem with final task dataset

After loading final project data

import pandas as pd
import numpy as np

import warnings
warnings.filterwarnings("ignore")
  
training_data = pd.read_csv('../data/final_project/training.csv')
test_data = pd.read_csv('../data/final_project/test_features.csv')
y_test = pd.read_csv('../data/final_project/y_test.csv')

print('The shape of the training dataset is:', training_data.shape)
print('The shape of the test dataset is:', test_data.shape)
print('The shape of the y_test is:', y_test.shape)

The shape of the training dataset is: (71538, 13) The shape of the test dataset is: (23846, 12) The shape of the y_test is: (23845, 1)

The number of samples for test features differs from y_test. Is it correct?

opened by agrigoriev 1

Corrected a typo in environment file and update main page

Description of changes: Fixed a typo in mlu-tab.yml and updated the main page with instructions.

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

opened by cemsaz 1
Upgrade lib versions

Description of changes: Library requirements are adjusted and an environment file: mlu-tab.yml is added.

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opened by cemsaz 1
Main page nb added

Main page notebook with GitHub links for the courses added.

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

opened by cemsaz 1
Fixing library versions and updating sagemaker and autoluon nbs

Description of changes:

-Added the requirements file and used/tested with all notebooks -Updated the sagemaker notebook: MLA-TAB-Lecture2-SageMaker.ipynb so that it uses the latest Sagemaker API calls for linear learner -Updated the AutoGluon notebook: MLA-TAB-Lecture3-AutoGluon.ipynb to use the latest API calls and new features

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opened by cemsaz 1
Fixing the link to final proj nb on readme

Link for the final project nb on readme page was broken. It is fixed.

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

opened by cemsaz 0
Updated nb names to match recordings

Notebook are renamed.

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opened by cemsaz 0
Update readme and logo

Update readme and logo

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opened by cemsaz 0
Update README.md

readme title update

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opened by cemsaz 0
Testing the model on data used to train it?

One thing we can do is to test the model with the data we used to train it, and use sklearn's metrics functions to examine the performance of the classifier.

Isn't this wrong? Shouldn't the data set be divided into training and validation sets?

opened by nyck33 0
Bump setuptools from 58.4.0 to 65.5.1
Bumps setuptools from 58.4.0 to 65.5.1.

Release notes

Sourced from setuptools's releases.

v65.5.1

No release notes provided.

v65.5.0

No release notes provided.

v65.4.1

No release notes provided.

v65.4.0

No release notes provided.

v65.3.0

No release notes provided.

v65.2.0

No release notes provided.

v65.1.1

No release notes provided.

v65.1.0

No release notes provided.

v65.0.2

No release notes provided.

v65.0.1

No release notes provided.

v65.0.0

No release notes provided.

v64.0.3

No release notes provided.

v64.0.2

No release notes provided.

v64.0.1

No release notes provided.

v64.0.0

No release notes provided.

v63.4.3

No release notes provided.

v63.4.2

No release notes provided.

... (truncated)

Changelog

Sourced from setuptools's changelog.

v65.5.1

Misc ^^^^

#3638: Drop a test dependency on the mock package, always use :external+python:py:mod:unittest.mock -- by :user:hroncok

#3659: Fixed REDoS vector in package_index.

v65.5.0

Changes ^^^^^^^

#3624: Fixed editable install for multi-module/no-package src-layout projects.

#3626: Minor refactorings to support distutils using stdlib logging module.

Documentation changes ^^^^^^^^^^^^^^^^^^^^^

#3419: Updated the example version numbers to be compliant with PEP-440 on the "Specifying Your Project’s Version" page of the user guide.

Misc ^^^^

#3569: Improved information about conflicting entries in the current working directory and editable install (in documentation and as an informational warning).

#3576: Updated version of validate_pyproject.

v65.4.1

Misc ^^^^

#3613: Fixed encoding errors in expand.StaticModule when system default encoding doesn't match expectations for source files.

#3617: Merge with pypa/distutils@6852b20 including fix for pypa/distutils#181.

v65.4.0

Changes ^^^^^^^

#3609: Merge with pypa/distutils@d82d926 including support for DIST_EXTRA_CONFIG in pypa/distutils#177.

v65.3.0

... (truncated)

Commits

a462cb5 Bump version: 65.5.0 → 65.5.1

de35d8b Merge pull request #3656 from bmorris3/typos

58e23de Update changelog. Ref #3659.

43a9c9b Limit the amount of whitespace to search/backtrack. Fixes #3659.

5791343 Add test capturing failed expectation. Ref #3659.

1f97905 ⚫ Fade to black.

6254567 Remove workaround for emacs.

729b180 ⚫ Fade to black.

c068081 Typo corrections

f777a40 Suppress deprecation warning in --rsyncdir. Workaround for #3655.

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dependencies
opened by dependabot[bot] 0
Bump wheel from 0.37.0 to 0.38.1
Bumps wheel from 0.37.0 to 0.38.1.

Changelog

Sourced from wheel's changelog.

Release Notes

UNRELEASED

Updated vendored packaging to 22.0

0.38.4 (2022-11-09)

Fixed PKG-INFO conversion in bdist_wheel mangling UTF-8 header values in METADATA (PR by Anderson Bravalheri)

0.38.3 (2022-11-08)

Fixed install failure when used with --no-binary, reported on Ubuntu 20.04, by removing setup_requires from setup.cfg

0.38.2 (2022-11-05)

Fixed regression introduced in v0.38.1 which broke parsing of wheel file names with multiple platform tags

0.38.1 (2022-11-04)

Removed install dependency on setuptools

The future-proof fix in 0.36.0 for converting PyPy's SOABI into a abi tag was faulty. Fixed so that future changes in the SOABI will not change the tag.

0.38.0 (2022-10-21)

Dropped support for Python < 3.7

Updated vendored packaging to 21.3

Replaced all uses of distutils with setuptools

The handling of license_files (including glob patterns and default values) is now delegated to setuptools>=57.0.0 (#466). The package dependencies were updated to reflect this change.

Fixed potential DoS attack via the WHEEL_INFO_RE regular expression

Fixed ValueError: ZIP does not support timestamps before 1980 when using SOURCE_DATE_EPOCH=0 or when on-disk timestamps are earlier than 1980-01-01. Such timestamps are now changed to the minimum value before packaging.

0.37.1 (2021-12-22)

Fixed wheel pack duplicating the WHEEL contents when the build number has changed (#415)

Fixed parsing of file names containing commas in RECORD (PR by Hood Chatham)

0.37.0 (2021-08-09)

Added official Python 3.10 support

Updated vendored packaging library to v20.9

... (truncated)

Commits

6f1608d Created a new release

cf8f5ef Moved news item from PR #484 to its proper place

9ec2016 Removed install dependency on setuptools (#483)

747e1f6 Fixed PyPy SOABI parsing (#484)

7627548 [pre-commit.ci] pre-commit autoupdate (#480)

7b9e8e1 Test on Python 3.11 final

a04dfef Updated the pypi-publish action

94bb62c Fixed docs not building due to code style changes

d635664 Updated the codecov action to the latest version

fcb94cd Updated version to match the release

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dependencies
opened by dependabot[bot] 0
Bump numpy from 1.19.5 to 1.22.0
Bumps numpy from 1.19.5 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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dependencies
opened by dependabot[bot] 0
Fix Typo

Issue #, if available:

Description of changes: corrected typo - 'bar' from 'box'

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opened by Stallians 2

Owner

AWS Samples

GitHub

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

Related tags

Overview

Machine Learning University: Accelerated Tabular Data Class

YouTube

Course Overview

Contribute

License

Comments

v65.5.1

v65.5.0

v65.4.1

v65.4.0

v65.3.0

v65.2.0

v65.1.1

v65.1.0

v65.0.2

v65.0.1

v65.0.0

v64.0.3

v64.0.2

v64.0.1

v64.0.0

v63.4.3

v63.4.2

v65.5.1

v65.5.0

v65.4.1

v65.4.0

Release Notes

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Owner

AWS Samples

A standard framework for modelling Deep Learning Models for tabular data

The official implementation of the paper, "SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning"

A framework for attentive explainable deep learning on tabular data

AWS provides a Python SDK, "Boto3" ,which can be used to access the AWS-account from the local.

Implementation of TabTransformer, attention network for tabular data, in Pytorch

Boosted neural network for tabular data

The official PyTorch implementation of recent paper - SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Job-Recommend-Competition - Vectorwise Interpretable Attentions for Multimodal Tabular Data

Research on Tabular Deep Learning (Python package & papers)

Calculates carbon footprint based on fuel mix and discharge profile at the utility selected. Can create graphs and tabular output for fuel mix based on input file of series of power drawn over a period of time.

[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets

PyTorch implementation for OCT-GAN Neural ODE-based Conditional Tabular GANs (WWW 2021)

The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

The implementation of the algorithm in the paper "Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data" published in ICML 2020.

GPU-Accelerated Deep Learning Library in Python

Accelerated deep learning R&D

meProp: Sparsified Back Propagation for Accelerated Deep Learning

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

meProp: Sparsified Back Propagation for Accelerated Deep Learning (ICML 2017)

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio