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Personal Artificial Intelligence Resources List
https://github.com/eug/discriminative-ai-resources

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Personal Artificial Intelligence Resources List

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# discriminative-ai-resources



Personal Discriminative Artificial Intelligence Resources List.



## Contents

* [Pre-Trained Models](#pre-trained-models)

* [Deep Learning](#deep-learning)

* [General Purpose Machine Learning](#general-purpose-machine-learning)

* [Natural Language Processing](#natural-language-processing)

* [Time Series Forecasting](#time-series-forecasting)

* [Causal Inference](#causal-inference)

* [Statistical and Probabilistic Modelling](#statistical-and-probabilistic-modelling)

* [Auto Machine Learning](#auto-machine-learning)

* [Feature Engineering](#feature-engineering)

* [Model Management](#model-management)

* [Diagnostic, Inpection or Interpretation](#diagnostic-inpection-or-interpretation)

* [Data Visualization](#data-visualization)

* [Auto Data Visualization](#auto-data-visualization)

* [DataFrame Libraries](#dataframe-libraries)

* [Misc](#misc)

* [Tutorials and Examples](#tutorials-and-examples)

* [Lists](#lists)



## Pre-Trained Models

- [audio-pretrained-model](https://github.com/balavenkatesh3322/audio-pretrained-model) - A collection of Audio and Speech pre-trained models.

- [awesome-deeplearning](https://endymecy.github.io/awesome-deeplearning-resources/pre_trained.html) - Pre-trained models from the awesome-deeplearning repository.

- [camelot](https://github.com/camelot-dev/camelot) - A Python library to extract tabular data from PDFs.

- [coreml-models](https://github.com/likedan/Awesome-CoreML-Models) - Largest list of models for Core ML (for iOS 11+).

- [cv-pretrained-model](https://github.com/balavenkatesh3322/CV-pretrained-model) - A collection of computer vision pre-trained models.

- [efficientnet-pytorch](https://github.com/lukemelas/EfficientNet-PyTorch) - A PyTorch implementation of EfficientNet and EfficientNetV2.

- [huggingface](https://huggingface.co/models) - Browse the model hub to discover, experiment and contribute to new state of the art models.

- [layout-parser](https://github.com/Layout-Parser/layout-parser) - A unified toolkit for Deep Learning Based Document Image Analysis.

- [mmf](https://mmf.sh/docs/notes/pretrained_models) - A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

- [modelzoo](https://modelzoo.co) - Models and code that perform audio processing, speech synthesis, and other audio related tasks.

- [nlp-pretrained-model](https://github.com/balavenkatesh3322/NLP-pretrained-model) - A collection of Natural language processing pre-trained models.

- [nlp-recipes](https://github.com/microsoft/nlp-recipes) - Natural Language Processing Best Practices & Examples.

- [openvino](https://docs.openvinotoolkit.org/latest/omz_models_group_intel.html) - Pre-trained Deep Learning models and demos (high quality and extremely fast).

- [PaddlePaddle](https://github.com/PaddlePaddle/PaddleHub) - Awesome pre-trained models toolkit based on PaddlePaddle.

- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) - Awesome multilingual OCR toolkits based on PaddlePaddle.

- [pyannote-audio](https://github.com/pyannote/pyannote-audio-hub) - Neural building blocks for speaker and speech detection.

- [pytorch-image-models](https://github.com/rwightman/pytorch-image-models) - PyTorch image models, scripts, pretrained weights

- [stylegan](https://github.com/justinpinkney/awesome-pretrained-stylegan) - A collection of pre-trained StyleGAN models to download.

- [tabula](https://github.com/tabulapdf/tabula) - Tabula is a tool for liberating data tables trapped inside PDF files.

- [tfhub](https://tfhub.dev/) - Search and discover hundreds of trained, ready-to-deploy machine learning models.

- [unilm](https://github.com/microsoft/unilm) - Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities



## Deep Learning

- [amazon-dsstne](https://github.com/amzn/amazon-dsstne) - Deep Scalable Sparse Tensor Network Engine.

- [caffe](https://github.com/BVLC/caffe) - A fast open framework for deep learning. 

- [chainer](https://github.com/chainer/chainer) - A flexible framework of neural networks for deep learning.

- [cntk](https://github.com/Microsoft/cntk) - An open source deep-learning toolkit.

- [deepdetect](https://github.com/jolibrain/deepdetect) - It makes state of the art machine learning easy to work with and integrate into existing applications.

- [deeplearning4j](https://github.com/deeplearning4j/deeplearning4j) - Open-source, distributed, scientific computing for the JVM.

- [fastai](https://github.com/fastai/fastai) - The fast.ai deep learning library, lessons, and tutorials.

- [gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.

- [keras](https://github.com/keras-team/keras) - Deep Learning for humans.

- [mxnet](https://github.com/apache/incubator-mxnet) - A flexible and efficient library for deep learning.

- [neon](https://github.com/NervanaSystems/neon) - Intel® Nervana™ reference deep learning framework.

- [neupy](https://github.com/itdxer/neupy) - NeuPy is a Python library for Artificial Neural Networks and Deep Learning.

- [neural-enhance](https://github.com/alexjc/neural-enhance) - Super Resolution for images using deep learning. 

- [Paddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning.

- [singa](https://github.com/apache/incubator-singa) - Distributed deep learning system.

- [sonnet](https://github.com/deepmind/sonnet) - TensorFlow-based neural network library.

- [swflow](https://github.com/tensorflow/skflow) - Simplified interface for TensorFlow for Deep Learning.

- [tensorflow](https://github.com/tensorflow/tensorflow) - Computation using data flow graphs for scalable - machine learning.

- [tensorpack](https://github.com/tensorpack/tensorpack) - A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility.

- [tflearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow.



## General Purpose Machine Learning

- [aerosolve](https://github.com/airbnb/aerosolve) - A machine learning package built for humans.

- [AmpliGraph](https://github.com/Accenture/AmpliGraph) - Python library for Representation Learning on Knowledge Graphs.

- [catboost](https://github.com/catboost/catboost) - An open-source gradient boosting library with categorical features support.

- [dmtk](https://github.com/Microsoft/DMTK) - Microsoft Distributed Machine Learning Toolkit.

- [fastFM](https://github.com/ibayer/fastFM) - fastFM: A Library for Factorization Machines.

- [fklearn](https://github.com/nubank/fklearn) - Functional Machine Learning.

- [h2o](https://github.com/h2oai/h2o-3) - Open Source Fast Scalable Machine Learning Platform For Smarter Applications.

- [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn) - A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning.

- [imodels](https://github.com/csinva/imodels) - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling.

- [JSAT](https://github.com/EdwardRaff/JSAT) - Java Statistical Analysis Tool, a Java library for Machine Learning.

- [libffm](https://github.com/guestwalk/libffm) - A Library for Field-aware Factorization Machines.

- [libfm](https://github.com/srendle/libfm) - Library for factorization machines.

- [LightGBM](https://github.com/microsoft/LightGBM) - A fast, distributed, high performance gradient boosting based on decision tree algorithms.

- [madlib](https://github.com/apache/madlib) - It is an open-source library for scalable in-database analytics.

- [metric-learn](https://github.com/scikit-learn-contrib/metric-learn) - Metric learning algorithms in Python.

- [mlens](https://github.com/flennerhag/mlens) - ML-Ensemble – high performance ensemble learning.

- [mllib](https://github.com/apache/spark/tree/master/mllib) - MLlib is Apache Spark's scalable machine learning library.

- [moa](https://github.com/Waikato/moa) - It is an open source framework for Big Data stream mining.

- [orange3](https://github.com/biolab/orange3) -  Interactive data analysis.

- [pycobra](https://github.com/bhargavvader/pycobra) - Python library implementing ensemble methods and visualisation tools including Voronoi tesselations.

- [pyod](https://github.com/yzhao062/pyod) - A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).

- [rep](https://github.com/yandex/rep) - Machine Learning toolbox for Humans.

- [river](https://github.com/online-ml/river) - Online machine learning in Python.

- [scikit-learn](https://github.com/scikit-learn/scikit-learn) - Machine learning in Python.

- [shogun](https://github.com/shogun-toolbox/shogun) - Unified and efficient Machine Learning since 1999.

- [weka](https://svn.cms.waikato.ac.nz/svn/weka/) - It is a collection of machine learning algorithms for data mining tasks.

- [xgboost](https://github.com/dmlc/xgboost) - Scalable, Portable and Distributed Gradient Boosting Library.



## Natural Language Processing

- [allennlp](https://github.com/allenai/allennlp) - An open-source NLP research library, built on PyTorch.

- [anago](https://github.com/Hironsan/anago) - A Python library for sequence labeling implemented in Keras.

- [CoreNLP](https://github.com/stanfordnlp/CoreNLP) - Stanford CoreNLP: A Java suite of core NLP tools.

- [dimsum16](https://github.com/jbjorne/DiMSUM2016) - Detecting Minimal Semantic Units and their Meanings - (DiMSUM).

- [finetune](https://github.com/IndicoDataSolutions/finetune) - Scikit-learn style model finetuning for NLP.

- [flair](https://github.com/flairNLP/flair) - A very simple framework for state-of-the-art NLP.

- [flashtext](https://github.com/vi3k6i5/flashtext) - Extract Keywords from sentence or Replace keywords in sentences.

- [fuzzywuzzy](https://github.com/seatgeek/fuzzywuzzy) - Fuzzy String Matching in Python.

- [gensim](https://github.com/RaRe-Technologies/gensim) - Topic Modelling for Humans.

- [gluon](https://github.com/dmlc/gluon-nlp) - A toolkit that enables easy text preprocessing to help you speed up your NLP research.

- [Kashgari](https://github.com/BrikerMan/Kashgari) - NLP Transfer learning framework for text-labeling and text-classification.

- [magnitude](https://github.com/plasticityai/magnitude) - A fast, efficient universal vector embedding utility package. 

- [mallet](http://mallet.cs.umass.edu/) - It is a Java-based package for machine learning applications to text.

- [nltk](https://github.com/nltk/nltk) - Natural Language Toolkit.

- [pattern](https://github.com/clips/pattern) - Web mining module for Python, with tools for scraping, NLP, ML, network analysis and viz.

- [polyglot](https://github.com/aboSamoor/polyglot) - Multilingual text (NLP) processing toolkit.

- [rasa](https://github.com/RasaHQ/rasa) - Open source machine learning framework to automate text- and voice-based conversations.

- [senpy](https://github.com/gsi-upm/senpy) - A sentiment and emotion analysis server in Python.

- [snips-nlu](https://github.com/snipsco/snips-nlu) - Snips Python library to extract meaning from text.

- [spaCy](https://github.com/explosion/spaCy) - Industrial-strength Natural Language Processing (NLP) in Python.

- [textacy](https://github.com/chartbeat-labs/textacy) - A Python library for performing a variety of NLP tasks.

- [TextBlob](https://github.com/sloria/TextBlob) - Simple, Pythonic, text processing. 

- [textgenrnn](https://github.com/minimaxir/textgenrnn) - Easily train your own text-generating neural network on any text dataset.

- [word2vec](https://github.com/danielfrg/word2vec) - Python interface to Google word2vec.



## Time Series Forecasting

- [auto-ts](https://github.com/AutoViML/Auto_TS) - Automatically build models on time series datasets with a single line of code.

- [darts](https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.

- [pmdarima](https://github.com/alkaline-ml/pmdarima) - Time series analysis (including auto arima) for Python.

- [prophet](https://github.com/facebook/prophet) - A procedure for forecasting time series data based on an additive model.

- [pyflux](https://github.com/RJT1990/pyflux) - Open source time series library for Python.

- [pysts](https://github.com/johannfaouzi/pyts) - A Python package for time series classification.

- [scikit-hts](https://github.com/carlomazzaferro/scikit-hts) - Hierarchical time series forecasting for humans.

- [sktime-dl](https://github.com/sktime/sktime-dl) - A sktime companion package for deep learning based on TensorFlow.

- [sktime](https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series.

- [statsmodels.tsa](https://github.com/statsmodels/statsmodels) - Time Series analysis from statsmodels package.

- [traces](https://github.com/datascopeanalytics/traces) - A Python library for unevenly-spaced time series analysis.

- [tsai](https://github.com/timeseriesAI/tsai) - Time series Timeseries Deep Learning Pytorch fastai.

- [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series.



## Causal Inference

- [causallib](https://github.com/IBM/causallib) - Modular causal inference analysis and model evaluations.

- [causalml](https://github.com/uber/causalml) - Uplift modeling and causal inference with machine learning algorithms.

- [causalnex](https://github.com/quantumblacklabs/causalnex) - Helps data scientists to infer causation rather than observing correlation.

- [dowhy](https://github.com/microsoft/dowhy) - A Python library for causal inference that supports explicit modeling and testing of causal assumptions.

- [EconML](https://github.com/microsoft/EconML) - Automated Learning and Intelligence for Causation and Economics.



## Statistical and Probabilistic Modelling

- [BayesianOptimization](https://github.com/fmfn/BayesianOptimization) - A Python implementation of global optimization with gaussian processes.

- [edward](https://github.com/blei-lab/edward) - A probabilistic programming language in TensorFlow.

- [hmmlearn](https://github.com/hmmlearn/hmmlearn) - Hidden Markov Models in Python, with scikit-learn like API.

- [lifelines](https://github.com/CamDavidsonPilon/lifelines) - Survival analysis in Python.

- [lifetimes](https://github.com/CamDavidsonPilon/lifetimes) - Lifetime value in Python.

- [lightweight_mmm](https://github.com/google/lightweight_mmm) - Easy to use Bayesian Marketing Mix Modeling (MMM).

- [mord](https://github.com/fabianp/mord) - Ordinal regression algorithms.

- [pomegranate](https://github.com/jmschrei/pomegranate) - Fast, flexible and easy to use probabilistic modelling in Python.

- [pyglmnet](https://github.com/glm-tools/pyglmnet) - Python implementation of elastic-net regularized generalized linear models.

- [pymc3](https://github.com/pymc-devs/pymc3) - Probabilistic Programming in Python.

- [python-mle](https://github.com/ibab/python-mle) - A Python package for performing Maximum Likelihood Estimates.

- [RoBo](https://github.com/automl/RoBO) - A Robust Bayesian Optimization framework.

- [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.

- [tea-lang](https://github.com/emjun/tea-lang) - DSL for experimental design and statistical analysis.

- [pingouin](https://pingouin-stats.org/) - Statistical package in Python based on Pandas.



## Auto Machine Learning

- [adanet](https://github.com/tensorflow/adanet) - AdaNet is a lightweight TensorFlow-based framework for AutoML.

- [AlphaPy](https://github.com/ScottfreeLLC/AlphaPy) - Automated Machine Learning AutoML for Python.

- [auto-sklearn](https://github.com/automl/auto-sklearn) - Automated Machine Learning with scikit-learn.

- [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for analytics & production.

- [autogluon](https://github.com/awslabs/autogluon) - AutoML for Text, Image, and Tabular Data.

- [autokeras](https://github.com/jhfjhfj1/autokeras) - Accessible AutoML for deep learning.

- [automl-gs](https://github.com/minimaxir/automl-gs) - AutoML tool that offers a zero code/model definition interface to getting an optimized model.

- [diaml](https://github.com/chasedehan/diaml) - Semi-automated machine learning pipelines.

- [FLAML](https://github.com/microsoft/FLAML) - A fast and lightweight AutoML library.

- [ludwig](https://github.com/uber/ludwig) - Ludwig is a toolbox that allows to train deep learning models without coding.

- [MLBox](https://github.com/AxeldeRomblay/MLBox) - It is a powerful Automated Machine Learning python library.

- [nni](https://github.com/microsoft/nni) - An open source AutoML toolkit for automate machine learning lifecycle.

- [onepanel-automl](https://github.com/onepanelio/automl) - Onepanel AutoML.

- [optuna](https://github.com/pfnet/optuna) - A hyperparameter optimization framework.

- [pycaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python.

- [SMAC3](https://github.com/automl/SMAC3) - Sequential Model-based Algorithm Configuration.

- [TPOT](https://github.com/EpistasisLab/tpot) - Tree-Based Pipeline Optimization Tool.

- [TransmogrifAI](https://github.com/salesforce/TransmogrifAI) - Automated machine learning for structured data. 

- [xcessiv](https://github.com/reiinakano/xcessiv) - A web-based application for automated hyperparameter tuning and stacked ensembling in Python.



## Feature Engineering

- [categorical-encoding](https://github.com/scikit-learn-contrib/categorical-encoding) - A library of sklearn compatible categorical variable encoders.

- [datacleaner](https://github.com/rhiever/datacleaner) - A Python tool that automatically cleans data sets and readies them for analysis. 

- [feature-selector](https://github.com/WillKoehrsen/feature-selector) - Feature selector is a tool for dimensionality reduction of machine learning datasets.

- [featuretools](https://github.com/featuretools/featuretools) - Automated feature engineering.

- [gokinjo](https://github.com/momijiame/gokinjo) - A feature extraction library based on k-nearest neighbor algorithm in Python.

- [hypertools](https://github.com/ContextLab/hypertools) - A Python toolbox for gaining geometric insights into high-dimensional data.

- [umap](https://github.com/lmcinnes/umap) - A dimension reduction technique that can be used for visualisation.



## Model Management

- [BentoML](https://github.com/bentoml/BentoML) - Model serving made easy.

- [cog](https://github.com/replicate/cog) - Containers for machine learning.

- [cookiecutter-ds](https://github.com/drivendata/cookiecutter-data-science) - Logical and flexible project structure for doing and sharing data science work.

- [ds-process-management](https://github.com/jeongyoonlee/data-science-process-management) - Resources for Data Science Process management.

- [dvc](https://github.com/iterative/dvc) - Data & models versioning for ML projects, make them shareable and reproducible.

- [firefly](https://github.com/rorodata/firefly) - Function as a service.

- [hopsworks](https://github.com/logicalclocks/hopsworks) - Full-stack platform for scale-out data science.

- [kedro](https://github.com/quantumblacklabs/kedro) - A Python library for building robust production-ready data and analytics pipelines.

- [lore](https://github.com/instacart/lore) - A python framework to make machine learning approachable.

- [marvin](https://github.com/marvin-ai/marvin-python-toolbox) - The toolbox helps data scientists to develop, test, and run marvin engines.

- [metaflow](https://github.com/Netflix/metaflow) - Build and manage real-life data science projects with ease.

- [mlflow](https://github.com/mlflow/mlflow) - Open source platform for the machine learning lifecycle.

- [neptune](https://neptune.ai/) - Log, organize, compare, register, and share all your ML model metadata in a single place.



## Diagnostic, Inpection or Interpretation

- [anchor](https://github.com/marcotcr/anchor) - High-Precision Model-Agnostic Explanations.

- [ann-visualizer](https://github.com/Prodicode/ann-visualizer) - A python library for visualizing Artificial Neural Networks with Keras.

- [awesome-interpretable-machine-learning](https://github.com/lopusz/awesome-interpretable-machine-learning) - Opinionated list of resources facilitating model interpretability.

- [eli5](https://github.com/TeamHG-Memex/eli5) - A library for debugging/inspecting machine learning classifiers and explaining their predictions.

- [explainerdashboard](https://github.com/oegedijk/explainerdashboard) - Quickly build Explainable AI dashboards.

- [interpret](https://github.com/microsoft/interpret) - Fit interpretable models. Explain blackbox machine learning.

- [lime](https://github.com/marcotcr/lime) - Explaining the predictions of any machine learning classifier.

- [lucid](https://github.com/tensorflow/lucid) - A collection of infrastructure and tools for research in neural network interpretability.

- [PDPbox](https://github.com/SauceCat/PDPbox) - Python partial dependence plot toolbox.

- [SHAP](https://github.com/slundberg/shap) - A unified approach to explain the output of any machine learning model.

- [what-if-tool](https://github.com/tensorflow/tensorboard/tree/master/tensorboard/plugins/interactive_inference) - Easy-to-use interface for expanding understanding of a black-box classification/regression model.

- [yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) - Visual analysis and diagnostic tools to facilitate machine learning model selection.



## Data Visualization

- [altair](https://github.com/altair-viz/altair) - Declarative statistical visualization library for Python.

- [animatplot](https://github.com/t-makaro/animatplot) - A python package for animating plots build on matplotlib.

- [bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.

- [chartify](https://github.com/spotify/chartify) - Python library that makes it easy for data scientists to create charts.

- [dash](https://github.com/plotly/dash) - Interactive, Reactive Web Apps for Python.

- [folium](https://github.com/python-visualization/folium) - Python Data to Leaflet.js Maps.

- [ft-visual-vocabulary](https://github.com/Financial-Times/chart-doctor/tree/main/visual-vocabulary) - The core of a newsroom-wide training session aimed at improving chart literacy.

- [holoviews](https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.

- [ipyvolume](https://github.com/maartenbreddels/ipyvolume) - 3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL.

- [matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with Python.

- [plotnine](https://github.com/has2k1/plotnine) - A grammar of graphics for Python.

- [scattertext](https://github.com/JasonKessler/scattertext) - Beautiful visualizations of how language differs among document types. 

- [scikit-plot](https://github.com/reiinakano/scikit-plot) - An intuitive library to add plotting functionality to scikit-learn objects.

- [seaborn](http://seaborn.pydata.org/) - Statistical data visualization.

- [speedml](https://github.com/Speedml/speedml) - Speedml is a Python package to speed start machine learning projects. 

- [streamlit](https://github.com/streamlit/streamlit) - The fastest way to build custom ML tools.

- [vega](https://github.com/vega/vega) - A visualization grammar.

- [veles](https://github.com/codilime/veles) - Binary data analysis and visualization tool.

- [vispy](https://github.com/vispy/vispy) - Interactive scientific visualization that is designed to be fast, scalable, and easy to use.

- [wordcloud](https://github.com/amueller/word_cloud) - A little word cloud generator in Python.



## Auto Data Visualization

- [AutoViz](https://github.com/AutoViML/AutoViz) - Automatically visualize any dataset, any size with a single line of code.

- [dataprep](https://github.com/sfu-db/dataprep) - The easiest way to prepare data in Python.

- [dtale](https://github.com/man-group/dtale) - Visualizer for pandas data structures.

- [PandasGUI](https://github.com/adamerose/PandasGUI) - A GUI for Pandas DataFrames.

- [pandas-profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects.

- [sweetviz](https://github.com/fbdesignpro/sweetviz) - Visualize and compare datasets, target values and associations, with one line of code.



## DataFrame Libraries

- [cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library.

- [dask](https://github.com/dask/dask) - Parallel computing with task scheduling.

- [datatables](https://github.com/h2oai/datatable) - A Python package for manipulating 2-dimensional tabular data structures.

- [modin](https://github.com/modin-project/modin) - Speed up your Pandas workflows by changing a single line of code.

- [pandas](https://pandas.pydata.org/) - Fast, powerful, flexible and easy to use open source data analysis and manipulation tool.

- [pandas_flavor](https://github.com/Zsailer/pandas_flavor) - The easy way to write your own flavor of Pandas.

- [sklearn-pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) - Pandas integration with sklearn.

- [terality](https://docs.terality.com/) -  Serverless data processing engine.

- [vaex](https://vaex.io/docs/index.html) - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python.



## Misc

- [deap](https://github.com/DEAP/deap) - Distributed Evolutionary Algorithms in Python

- [feather](https://github.com/wesm/feather) - Fast, interoperable binary data frame storage for Python and R.

- [gplearn](https://github.com/trevorstephens/gplearn) - Genetic Programming in Python.

- [PyGAD](https://github.com/ahmedfgad/GeneticAlgorithmPython) - Python 3 library for building the genetic algorithm and training machine learning algorithms.

- [gtdata](http://googletrends.github.io/data/) - Download and play with key datasets from Google Trend.

- [librosa](https://github.com/librosa/librosa) - Python library for audio and music analysis.

- [m2cgen](https://github.com/BayesWitnesses/m2cgen) - Transform ML models into a native code with zero dependencies 

- [mahout](https://github.com/apache/mahout) - It is a distributed linear algebra framework and mathematically expressive Scala DSL.

- [mlxtend](https://github.com/rasbt/mlxtend) - A library of extension and helper modules for Python's data analysis and machine learning libraries.

- [pythia](https://github.com/facebookresearch/pythia) - A modular framework for Visual Question Answering research from Facebook AI Research (FAIR).

- [snorkel](https://github.com/snorkel-team/snorkel) - A system for quickly generating training data with weak supervision.



## Tutorials and Examples

- [100 Days of ML Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code) - 100 Days of ML Coding.

- [BayesianModelling](https://github.com/markdregan/Bayesian-Modelling-in-Python) - A python tutorial on bayesian modeling techniques.

- [ds-ipython-notebooks](https://github.com/donnemartin/data-science-ipython-notebooks) - Data science Python notebooks.

- [EffectiveTensorflow](https://github.com/vahidk/EffectiveTensorflow) - TensorFlow tutorials and best practices.

- [kaggle-past-solutions](https://github.com/EliotAndres/kaggle-past-solutions) - A searchable compilation of Kaggle past solutions.

- [MLAlgorithms](https://github.com/rushter/MLAlgorithms) - Minimal and clean examples of machine learning algorithms implementations.

- [MLFromScratch](https://github.com/eriklindernoren/ML-From-Scratch) - Machine Learning From Scratch.

- [MLPB](https://github.com/ben519/MLPB) - Machine Learning Problem Bible.

- [tf-models](https://github.com/tensorflow/models) - Models and examples built with TensorFlow.

- [Virgilio](https://github.com/virgili0/Virgilio) - Your new Mentor for Data Science E-Learning.



## Lists

- [awesome-datascience](https://github.com/bulutyazilim/awesome-datascience) - An awesome Data Science repository to learn and apply for real world problems.

- [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers) - The most cited deep learning papers.

- [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning) - A curated list of awesome Machine Learning frameworks, libraries and software.

- [Deep Learning Drizzle](https://github.com/kmario23/deep-learning-drizzle) - Learn Deep Lerning from exciting lectures.

- [Deep-Learning-Papers-Reading-Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap) - Deep Learning papers reading roadmap.

- [Deep-Learning-World](https://github.com/astorfi/Deep-Learning-World) - Organized Resources for Deep Learning Researchers and Developers.

- [ml4se](https://github.com/ZuzooVn/machine-learning-for-software-engineers) - A complete daily plan for studying to become a machine learning engineer. 

- [ossu-data-science](https://github.com/ossu/data-science) - Path to a free self-taught education in Data Science!

- [python-machine-learning-book](https://github.com/rasbt/python-machine-learning-book) - The "Python Machine Learning (1st edition)" book code repository and info resource.

- [OCEANIS](https://ethicsstandards.org/repository/) - List of AI and Autonomous and Intelligent Systems standards and standards in progress.