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Merge pull request #835 from AntoniosFl/add-pycaret

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Add PyCaret, a machine learning python library
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@ -190,7 +190,7 @@ Further resources:
## C++
<a name="cpp-computer-vision"></a>
#### Computer Vision
#### Computer Vision
* [DLib](http://dlib.net/imaging.html) - DLib has C++ and Python interfaces for face detection and training general object detectors.
* [EBLearn](http://eblearn.sourceforge.net/) - Eblearn is an object-oriented C++ library that implements various machine learning models **[Deprecated]**
@ -221,6 +221,7 @@ Further resources:
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
* [ParaMonte](https://github.com/cdslaborg/paramonte) - A general-purpose library with C/C++ interface for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found [here](https://www.cdslab.org/paramonte/).
* [proNet-core](https://github.com/cnclabs/proNet-core) - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python that automates machine learning workflows.
* [PyCUDA](https://mathema.tician.de/software/pycuda/) - Python interface to CUDA
* [ROOT](https://root.cern.ch) - A modular scientific software framework. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualization and storage.
* [shark](http://image.diku.dk/shark/sphinx_pages/build/html/index.html) - A fast, modular, feature-rich open-source C++ machine learning library.
@ -292,9 +293,9 @@ Further resources:
#### General-Purpose Machine Learning
* [tech.ml](https://github.com/techascent/tech.ml) - A machine learning platform based on tech.ml.dataset, supporting not just ml algorithms, but also relevant ETL processing; wraps multiple machine learning libraries
* [clj-ml](https://github.com/joshuaeckroth/clj-ml/) - A machine learning library for Clojure built on top of Weka and friends.
* [clj-ml](https://github.com/joshuaeckroth/clj-ml/) - A machine learning library for Clojure built on top of Weka and friends.
* [clj-boost](https://gitlab.com/alanmarazzi/clj-boost) - Wrapper for XGBoost
* [Touchstone](https://github.com/ptaoussanis/touchstone) - Clojure A/B testing library.
* [Touchstone](https://github.com/ptaoussanis/touchstone) - Clojure A/B testing library.
* [Clojush](https://github.com/lspector/Clojush) - The Push programming language and the PushGP genetic programming system implemented in Clojure.
* [lambda-ml](https://github.com/cloudkj/lambda-ml) - Simple, concise implementations of machine learning techniques and utilities in Clojure.
* [Infer](https://github.com/aria42/infer) - Inference and machine learning in Clojure. **[Deprecated]**
@ -310,12 +311,12 @@ Further resources:
* [Deep Diamond](https://github.com/uncomplicate/deep-diamond) - A fast Clojure Tensor & Deep Learning library
* [jutsu.ai](https://github.com/hswick/jutsu.ai) - Clojure wrapper for deeplearning4j with some added syntactic sugar.
* [cortex](https://github.com/originrose/cortex) - Neural networks, regression and feature learning in Clojure.
* [Flare](https://github.com/aria42/flare) - Dynamic Tensor Graph library in Clojure (think PyTorch, DynNet, etc.)
* [Flare](https://github.com/aria42/flare) - Dynamic Tensor Graph library in Clojure (think PyTorch, DynNet, etc.)
* [dl4clj](https://github.com/yetanalytics/dl4clj) - Clojure wrapper for Deeplearning4j.
<a name="clojure-data-analysis--data-visualization"></a>
#### Data Analysis
* [tech.ml.dataset](https://github.com/techascent/tech.ml.dataset) - Clojure dataframe library and pipeline for data processing and machine learning
#### Data Analysis
* [tech.ml.dataset](https://github.com/techascent/tech.ml.dataset) - Clojure dataframe library and pipeline for data processing and machine learning
* [Tablecloth](https://github.com/scicloj/tablecloth) - A dataframe grammar wrapping tech.ml.dataset, inspired by several R libraries
* [Panthera](https://github.com/alanmarazzi/panthera) - Clojure API wrapping Python's Pandas library
* [Incanter](http://incanter.org/) - Incanter is a Clojure-based, R-like platform for statistical computing and graphics.
@ -328,10 +329,10 @@ Further resources:
* [Saite](https://github.com/jsa-aerial/saite) - Clojure(Script) client/server application for dynamic interactive explorations and the creation of live shareable documents capturing them using Vega/Vega-Lite, CodeMirror, markdown, and LaTeX
* [Oz](https://github.com/metasoarous/oz) - Data visualisation using Vega/Vega-Lite and Hiccup, and a live-reload platform for literate-programming
* [Envision](https://github.com/clojurewerkz/envision) - Clojure Data Visualisation library, based on Statistiker and D3.
* [Pink Gorilla Notebook](https://github.com/pink-gorilla/gorilla-notebook) - A Clojure/Clojurescript notebook application/-library based on Gorilla-REPL
* [Pink Gorilla Notebook](https://github.com/pink-gorilla/gorilla-notebook) - A Clojure/Clojurescript notebook application/-library based on Gorilla-REPL
* [clojupyter](https://github.com/clojupyter/clojupyter) - A Jupyter kernel for Clojure - run Clojure code in Jupyter Lab, Notebook and Console.
* [notespace](https://github.com/scicloj/notespace) - Notebook experience in your Clojure namespace
* [Delight](https://github.com/datamechanics/delight) - A listener that streams your spark events logs to delight, a free and improved spark UI
* [notespace](https://github.com/scicloj/notespace) - Notebook experience in your Clojure namespace
* [Delight](https://github.com/datamechanics/delight) - A listener that streams your spark events logs to delight, a free and improved spark UI
<a name="clojure-interop"></a>
#### Interop
@ -344,7 +345,7 @@ Further resources:
<a name="clojure-misc"></a>
#### Misc
* [Neanderthal](https://neanderthal.uncomplicate.org/) - Fast Clojure Matrix Library (native CPU, GPU, OpenCL, CUDA)
* [kixistats](https://github.com/MastodonC/kixi.stats) - A library of statistical distribution sampling and transducing functions
* [kixistats](https://github.com/MastodonC/kixi.stats) - A library of statistical distribution sampling and transducing functions
* [fastmath](https://github.com/generateme/fastmath) - A collection of functions for mathematical and statistical computing, macine learning, etc., wrapping several JVM libraries
* [matlib](https://github.com/atisharma/matlib) - a Clojure library of optimisation and control theory tools and convenience functions based on Neanderthal.
@ -390,7 +391,7 @@ Further resources:
<a name="fortran-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning
* [neural-fortran](https://github.com/modern-fortran/neural-fortran) - A parallel neural net microframework.
* [neural-fortran](https://github.com/modern-fortran/neural-fortran) - A parallel neural net microframework.
Read the paper [here](https://arxiv.org/abs/1902.06714).
<a name="fortran-data-analysis-visualization"></a>
@ -997,7 +998,7 @@ be
* [face_recognition](https://github.com/ageitgey/face_recognition) - Face recognition library that recognizes and manipulates faces from Python or from the command line.
* [dockerface](https://github.com/natanielruiz/dockerface) - Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container. **[Deprecated]**
* [Detectron](https://github.com/facebookresearch/Detectron) - FAIR's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. **[Deprecated]**
* [detectron2](https://github.com/facebookresearch/detectron2) - FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch deep learning framework.
* [detectron2](https://github.com/facebookresearch/detectron2) - FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch deep learning framework.
* [albumentations](https://github.com/albu/albumentations) - А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
* [pytessarct](https://github.com/madmaze/pytesseract) - Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded in images. Python-tesseract is a wrapper for [Google's Tesseract-OCR Engine](https://github.com/tesseract-ocr/tesseract).
* [imutils](https://github.com/jrosebr1/imutils) - A library containing Convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python.
@ -1066,7 +1067,7 @@ be
#### General-Purpose Machine Learning
* [Microsoft ML for Apache Spark](https://github.com/Azure/mmlspark) -> A distributed machine learning framework Apache Spark
* [Shapley](https://github.com/benedekrozemberczki/shapley) -> A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
* [Shapley](https://github.com/benedekrozemberczki/shapley) -> A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
* [igel](https://github.com/nidhaloff/igel) -> A delightful machine learning tool that allows you to train/fit, test and use models **without writing code**
* [ML Model building](https://github.com/Shanky-21/Machine_learning) -> A Repository Containing Classification, Clustering, Regression, Recommender Notebooks with illustration to make them.
* [ML/DL project template](https://github.com/PyTorchLightning/deep-learning-project-template)
@ -1123,7 +1124,7 @@ be
* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox.
* [Pyevolve](https://github.com/perone/Pyevolve) - Genetic algorithm framework. **[Deprecated]**
* [Caffe](https://github.com/BVLC/caffe) - A deep learning framework developed with cleanliness, readability, and speed in mind.
* [breze](https://github.com/breze-no-salt/breze) - Theano based library for deep and recurrent neural networks.
* [breze](https://github.com/breze-no-salt/breze) - Theano based library for deep and recurrent neural networks.
* [Cortex](https://github.com/cortexlabs/cortex) - Open source platform for deploying machine learning models in production.
* [pyhsmm](https://github.com/mattjj/pyhsmm) - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
* [SKLL](https://github.com/EducationalTestingService/skll) - A wrapper around scikit-learn that makes it simpler to conduct experiments.
@ -1191,7 +1192,7 @@ be
* [PyGrid](https://github.com/OpenMined/PyGrid/) - Peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft
* [sktime](https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series
* [OPFython](https://github.com/gugarosa/opfython) - A Python-inspired implementation of the Optimum-Path Forest classifier.
* [Opytimizer](https://github.com/gugarosa/opytimizer) - Python-based meta-heuristic optimization techniques.
* [Opytimizer](https://github.com/gugarosa/opytimizer) - Python-based meta-heuristic optimization techniques.
* [Gradio](https://github.com/gradio-app/gradio) - A Python library for quickly creating and sharing demos of models. Debug models interactively in your browser, get feedback from collaborators, and generate public links without deploying anything.
* [Hub](https://github.com/activeloopai/Hub) - Fastest unstructured dataset management for TensorFlow/PyTorch. Stream & version-control data. Store even petabyte-scale data in a single numpy-like array on the cloud accessible on any machine. Visit [activeloop.ai](https://activeloop.ai) for more info.
* [Synthia](https://github.com/dmey/synthia) - Multidimensional synthetic data generation in Python.
@ -1328,9 +1329,9 @@ be
* [Neuron](https://github.com/molcik/python-neuron) - Neuron is simple class for time series predictions. It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm. **[Deprecated]**
* [Data Driven Code](https://github.com/atmb4u/data-driven-code) - Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.
* [Machine Learning, Data Science and Deep Learning with Python](https://www.manning.com/livevideo/machine-learning-data-science-and-deep-learning-with-python) - LiveVideo course that covers machine learning, Tensorflow, artificial intelligence, and neural networks.
* [TResNet: High Performance GPU-Dedicated Architecture](https://github.com/mrT23/TResNet) - TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs.
* [TResNet: High Performance GPU-Dedicated Architecture](https://github.com/mrT23/TResNet) - TResNet models were designed and optimized to give the best speed-accuracy tradeoff out there on GPUs.
* [TResNet: Simple and powerful neural network library for python](https://github.com/zueve/neurolab) - Variety of supported types of Artificial Neural Network and learning algorithms.
* [Jina AI](https://jina.ai/) An easier way to build neural search in the cloud. Compatible with Jupyter Notebooks.
* [Jina AI](https://jina.ai/) An easier way to build neural search in the cloud. Compatible with Jupyter Notebooks.
* [sequitur](https://github.com/shobrook/sequitur) PyTorch library for creating and training sequence autoencoders in just two lines of code
<a name="python-kaggle-competition-source-code"></a>
@ -1684,7 +1685,7 @@ be
* [m2cgen](https://github.com/BayesWitnesses/m2cgen) - A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.
* [CML](https://github.com/iterative/cml) - A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.
* [Pythonizr](https://pythonizr.com) - An online tool to generate boilerplate machine learning code that uses scikit-learn.
* [Flyte](https://flyte.org/) - Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing.
* [Flyte](https://flyte.org/) - Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing.
<a name="books"></a>
## Books