> For the complete documentation index, see [llms.txt](https://irosyadi.gitbook.io/irosyadi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://irosyadi.gitbook.io/irosyadi/machine-learning/machine-learning-tool.md).

# Machine Learning Tools

## Machine Learning Toolbox

* [Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code](https://github.com/uber/ludwig)
* [a machine learning tool that allows to train, test and use models without writing code](https://github.com/nidhaloff/igel)
* [PyCaret is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of notebook environment.](https://pycaret.org/)
* [WEKA The workbench for machine learning](https://www.cs.waikato.ac.nz/ml/weka/)
* [igel](https://github.com/nidhaloff/igel) A machine learning tool that allows you to train/fit, test and use models without writing code
* [sktime](https://github.com/alan-turing-institute/sktime) A unified toolbox for machine learning with time series
* [FiftyOne](https://www.voxel51.com/docs/fiftyone) is an open source machine learning tool created by [Voxel51](https://voxel51.com) that helps you get closer to your data and ML models. With FiftyOne, you can rapidly experiment with your datasets, enabling you to search, sort, filter, visualize, analyze, and improve your datasets without excess wrangling or writing custom scripts.

## Machine Learning Deployment

* [Cortexlabs](https://github.com/cortexlabs/cortex/tree/master/examples) [Cortex](https://www.cortex.dev/)
* [Easily build web apps for ML and DS projects](https://www.streamlit.io/)

## Machine Learning Versioning Control

* [Replicate AI](https://replicate.ai/) versioning control for AI
* [Comet ML](https://www.comet.ml/site/) versioning control for ML

## Data Studio

* [Data Studio Google](https://datastudio.google.com/navigation/reporting)

## Machine Learning Ops

* [visenger/awesome-mlops: A curated list of references for MLOps](https://github.com/visenger/awesome-mlops)

## Machine Learning

* [GokuMohandas/MadeWithML: Learn how to responsibly deliver value with ML.](https://github.com/GokuMohandas/MadeWithML)
* [Home - Made With ML](https://madewithml.com/#foundations)

## Machine Learning Toolbox

* [Machine Learning Toolbox](https://amitness.com/toolbox/)
* [LabML Neural Networks](https://lab-ml.com/labml_nn/index.html) This is a collection of simple PyTorch implementations of neural networks and related algorithms.

## Machine Learning

* [alan-turing-institute/MLJ.jl at mlnews](https://github.com/alan-turing-institute/MLJ.jl?ref=mlnews) Julia Machine Learning Library
* [Rudrabha/Wav2Lip at mlnews](https://github.com/Rudrabha/Wav2Lip) Voice Wave to Lip Movement
* [Best AI Paper 2020](https://github.com/louisfb01/Best_AI_paper_2020)

## Machine Learning Tools

* [aimhubio/aim: Aim—a super-easy way to record, search and compare 1000s of ML training runs](https://github.com/aimhubio/aim)
  * [AimStack - Dev tools for AI engineers.](https://aimstack.io/)
* [Replicate–Version control for machine learning](https://replicate.ai/)

## Machine Learning Steps

* [Machine Learning Field Guide](https://www.kamwithk.com/machine-learning-field-guide-ckbbqt0iv025u5ks1a7kgjckx)
  * Importing Data
  * Data Cleaning
  * Visualisation
  * Modelling
  * Production

## Machine Learning

* [Annotation Tool](https://www.getmarkup.com/)

## Machine Learning

* [Norfair: an open source library for 2D object tracking](https://tryolabs.com/blog/2020/09/10/releasing-norfair-an-open-source-library-for-object-tracking/)
* [Data Preprocessing for ML](https://medium.com/better-programming/data-preprocessing-for-machine-learning-3822ace03ae6)

## Machine Learning Labeling

* [PixLab Annotate - Online Image Annotation, Labeling and Segmentation Tool](https://annotate.pixlab.io/)
* [DeNA/nota: Web application for image and video labeling and annotation](https://github.com/DeNA/nota)


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