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awesome-python-data-science

From gitlab
https://github.com/jacob98415/awesome-python-data-science

Last synced: about 11 hours ago
JSON representation

  • Web Scraping

  • Natural Language Processing

    • NLP

      • spaCy - Industrial-Strength Natural Language Processing.
      • gensim - Topic Modelling for Humans.
      • NLTK - Modules, data sets, and tutorials supporting research and development in Natural Language Processing.
      • CLTK - The Classical Language Toolkik.
      • skift - Scikit-learn wrappers for Python fastText. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Phonemizer - Simple text-to-phonemes converter for multiple languages.
      • flair - Very simple framework for state-of-the-art NLP.
      • pyMorfologik - Python binding for <a href="https://github.com/morfologik/morfologik-stemming">Morfologik</a>.

  • Machine Learning

    • Ensemble Methods

      • ML-Ensemble - High performance ensemble learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • stacked_generalization - Library for machine learning stacking generalization. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • vecstack - Python package for stacking (machine learning technique). <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Stacking - Simple and useful stacking library written in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • ML-Ensemble - High performance ensemble learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">

    • General Purpose Machine Learning

      • Shogun - Machine learning toolbox.
      • dlib - Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).
      • mlpack - A scalable C++ machine learning library (Python bindings).
      • xLearn - High Performance, Easy-to-use, and Scalable Machine Learning Package.
      • MLxtend - Extension and helper modules for Python's data analysis and machine learning libraries. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • cuML - RAPIDS Machine Learning Library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
      • causalml - Uplift modeling and causal inference with machine learning algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Karate Club - An unsupervised machine learning library for graph-structured data.
      • scikit-multilearn - Multi-label classification for python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • sklearn-expertsys - Highly interpretable classifiers for scikit learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • seqlearn - Sequence classification toolkit for Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • pystruct - Simple structured learning framework for Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Sparkit-learn - PySpark + scikit-learn = Sparkit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/spark_big.png" alt="Apache Spark based">
      • RuleFit - Implementation of the rulefit. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • pyGAM - Generalized Additive Models in Python.
      • Little Ball of Fur - A library for sampling graph structured data.
      • Reproducible Experiment Platform (REP) - Machine Learning toolbox for Humans. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • hyperlearn - 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • scikit-learn - Machine learning in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • metric-learn - Metric learning algorithms in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">

    • Gradient Boosting

      • CatBoost - An open-source gradient boosting on decision trees library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
      • XGBoost - Scalable, Portable, and Distributed Gradient Boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
      • ThunderGBM - Fast GBDTs and Random Forests on GPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
      • LightGBM - A fast, distributed, high-performance gradient boosting. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">

    • Automated Machine Learning

      • auto-sklearn - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • MLBox - A powerful Automated Machine Learning python library.
      • AutoGluon - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.

    • Imbalanced Datasets

      • imbalanced-learn - Module to perform under-sampling and over-sampling with various techniques. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • imbalanced-algorithms - Python-based implementations of algorithms for learning on imbalanced data. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/tf_big2.png" alt="sklearn">

    • Kernel Methods

      • liquidSVM - An implementation of SVMs.
      • ThunderSVM - A fast SVM Library on GPUs and CPUs. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
      • pyFM - Factorization machines in python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • fastFM - A library for Factorization Machines. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • tffm - TensorFlow implementation of an arbitrary order Factorization Machine. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/tf_big2.png" alt="sklearn">
      • scikit-rvm - Relevance Vector Machine implementation using the scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">

    • Random Forests

      • rpforest - A forest of random projection trees. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • sklearn-random-bits-forest - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).<img height="20" src="img/sklearn_big.png" alt="sklearn">

    • Extreme Learning Machine

      • Python-ELM - Extreme Learning Machine implementation in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Python Extreme Learning Machine (ELM) - A machine learning technique used for classification/regression tasks.
      • hpelm - High-performance implementation of Extreme Learning Machines (fast randomized neural networks). <img height="20" src="img/gpu_big.png" alt="GPU accelerated">

  • Time Series

    • NLP

      • dateutil - Powerful extensions to the standard datetime module
      • luminol - Anomaly Detection and Correlation library.
      • Prophet - Automatic Forecasting Procedure.
      • PyFlux - Open source time series library for Python.
      • Chaos Genius - ML powered analytics engine for outlier/anomaly detection and root cause analysis
      • darts - A python library for easy manipulation and forecasting of time series.
      • greykite - A flexible, intuitive, and fast forecasting library next.
      • statsforecast - Lightning fast forecasting with statistical and econometric models.
      • mlforecast - Scalable machine learning-based time series forecasting.
      • neuralforecast - Scalable machine learning-based time series forecasting.
      • bayesloop - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
      • tick - Module for statistical learning, with a particular emphasis on time-dependent modeling. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • tslearn - Machine learning toolkit dedicated to time-series data. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • maya - makes it very easy to parse a string and for changing timezones
      • sktime - A unified framework for machine learning with time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">

  • Optimization

    • NLP

      • OR-Tools - An open-source software suite for optimization by Google; provides a unified programming interface to a half dozen solvers: SCIP, GLPK, GLOP, CP-SAT, CPLEX, and Gurobi.
      • hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python.
      • nlopt - Library for nonlinear optimization (global and local, constrained or unconstrained).
      • Optuna - A hyperparameter optimization framework.
      • sklearn-deap - Use evolutionary algorithms instead of gridsearch in scikit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • scikit-opt - Heuristic Algorithms for optimization.
      • Talos - Hyperparameter Optimization for Keras Models.
      • BoTorch - Bayesian optimization in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • SMAC3 - Sequential Model-based Algorithm Configuration.
      • hyperopt-sklearn - Hyper-parameter optimization for sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • scikit-optimize - Sequential model-based optimization with a `scipy.optimize` interface.
      • Spearmint - Bayesian optimization.
      • Optunity - Is a library containing various optimizers for hyperparameter tuning.
      • PySwarms - A research toolkit for particle swarm optimization in Python.
      • Solid - A comprehensive gradient-free optimization framework written in Python.
      • Bayesian Optimization - A Python implementation of global optimization with gaussian processes.
      • sklearn-genetic-opt - Hyperparameters tuning and feature selection using evolutionary algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • sigopt_sklearn - SigOpt wrappers for scikit-learn methods. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • SafeOpt - Safe Bayesian Optimization.
      • Platypus - A Free and Open Source Python Library for Multiobjective Optimization.
      • GPflowOpt - Bayesian Optimization using GPflow. <img height="20" src="img/tf_big2.png" alt="sklearn">

  • Visualization

    • Interactive plots

      • Altair - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
      • Bokeh - Interactive Web Plotting for Python.
      • animatplot - A python package for animating plots built on matplotlib.
      • bqplot - Plotting library for IPython/Jupyter notebooks
      • pyecharts - Migrated from [Echarts](https://github.com/apache/echarts), a charting and visualization library, to Python's interactive visual drawing library.<img height="20" src="img/pyecharts.png" alt="pyecharts"> <img height="20" src="img/echarts.png" alt="echarts">

    • Map

      • folium - Makes it easy to visualize data on an interactive open street map

    • General Purposes

      • Matplotlib - Plotting with Python.
      • seaborn - Statistical data visualization using matplotlib.
      • prettyplotlib - Painlessly create beautiful matplotlib plots.
      • python-ternary - Ternary plotting library for Python with matplotlib.
      • missingno - Missing data visualization module for Python.
      • physt - Improved histograms.

    • Automatic Plotting

    • NLP

  • Deployment

    • NLP

      • fastapi - Modern, fast (high-performance), a web framework for building APIs with Python
      • binder - Enable sharing and execute Jupyter Notebooks
      • gradio - Create UIs for your machine learning model in Python in 3 minutes.
      • streamlit - Make it easy to deploy the machine learning model

  • Data Manipulation

    • Data Frames

      • pandas - Powerful Python data analysis toolkit.
      • blaze - NumPy and pandas interface to Big Data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • vaex - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
      • polars - A fast multi-threaded, hybrid-out-of-core DataFrame library.
      • datatable - Data.table for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
      • modin - Speed up your pandas workflows by changing a single line of code. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • swifter - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
      • koalas - pandas API on Apache Spark. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • cuDF - GPU DataFrame Library. <img height="20" src="img/pandas_big.png" alt="pandas compatible"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
      • xarray - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone indexing routines.
      • xpandas - Universal 1d/2d data containers with Transformers .functionality for data analysis by [The Alan Turing Institute](https://www.turing.ac.uk/).
      • pandas-log - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
      • pandasql - Allows you to query pandas DataFrames using SQL syntax. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • pysparkling - A pure Python implementation of Apache Spark's RDD and DStream interfaces. <img height="20" src="img/spark_big.png" alt="Apache Spark based">
      • sk-transformer - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • pandas_profiling - Create HTML profiling reports from pandas DataFrame objects
      • Arctic - High-performance datastore for time series and tick data.
      • pandas_flavor - A package that allows writing your own flavor of Pandas easily.
      • pandas-gbq - pandas Google Big Query. <img height="20" src="img/pandas_big.png" alt="pandas compatible">

    • Pipelines

      • SSPipe - Python pipe (|) operator with support for DataFrames and Numpy, and Pytorch.
      • sklearn-pandas - pandas integration with sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • dopanda - Hints and tips for using pandas in an analysis environment. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • meza - A Python toolkit for processing tabular data.
      • Hamilton - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.
      • pandas-ply - Functional data manipulation for pandas. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • Dplython - Dplyr for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
      • Prodmodel - Build system for data science pipelines.
      • pdpipe - Sasy pipelines for pandas DataFrames.
      • Dataset - Helps you conveniently work with random or sequential batches of your data and define data processing.

    • Data-centric AI

      • snorkel - A system for quickly generating training data with weak supervision.
      • cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
      • dataprep - Collect, clean, and visualize your data in Python with a few lines of code.

    • Synthetic Data

      • ydata-synthetic - A package to generate synthetic tabular and time-series data leveraging the state-of-the-art generative models. <img height="20" src="img/pandas_big.png" alt="pandas compatible">

  • Deep Learning

    • Others

      • DISCONTINUED PROJECTS
      • autograd - Efficiently computes derivatives of numpy code.
      • Caffe - A fast open framework for deep learning.
      • nnabla - Neural Network Libraries by Sony.
      • Tangent - Source-to-Source Debuggable Derivatives in Pure Python.
      • jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
      • Myia - Deep Learning framework (pre-alpha).
      • hipCaffe - The HIP port of Caffe. <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">

    • TensorFlow

      • TensorFlow - Computation using data flow graphs for scalable machine learning by Google. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • NeuPy - NeuPy is a Python library for Artificial Neural Networks and Deep Learning (previously: <img height="20" src="img/theano_big.png" alt="Theano compatible">). <img height="20" src="img/tf_big2.png" alt="sklearn">
      • Elephas - Distributed Deep learning with Keras & Spark. <img height="20" src="img/keras_big.png" alt="Keras compatible">
      • qkeras - A quantization deep learning library. <img height="20" src="img/keras_big.png" alt="Keras compatible">
      • Mesh TensorFlow - Model Parallelism Made Easier. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • TFLearn - Deep learning library featuring a higher-level API for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • Polyaxon - A platform that helps you build, manage and monitor deep learning models. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • tfdeploy - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • TensorFlow Fold - Deep learning with dynamic computation graphs in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • tensorlm - Wrapper library for text generation/language models at char and word level with RNN. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • keras-contrib - Keras community contributions. <img height="20" src="img/keras_big.png" alt="Keras compatible">
      • Hyperas - Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter. <img height="20" src="img/keras_big.png" alt="Keras compatible">
      • Hera - Train/evaluate a Keras model, and get metrics streamed to a dashboard in your browser. <img height="20" src="img/keras_big.png" alt="Keras compatible">
      • Spektral - Deep learning on graphs. <img height="20" src="img/keras_big.png" alt="Keras compatible">
      • Ludwig - A toolbox that allows one to train and test deep learning models without the need to write code. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • Sonnet - TensorFlow-based neural network library. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • tensorpack - A Neural Net Training Interface on TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • tensorflow-upstream - TensorFlow ROCm port. <img height="20" src="img/tf_big2.png" alt="sklearn"> <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
      • TensorLayer - Deep Learning and Reinforcement Learning Library for Researcher and Engineer. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • TensorLight - A high-level framework for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">

    • PyTorch

      • torchvision - Datasets, Transforms, and Models specific to Computer Vision. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • torchtext - Data loaders and abstractions for text and NLP. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • torchaudio - An audio library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • ignite - High-level library to help with training neural networks in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • Catalyst - High-level utils for PyTorch DL & RL research. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • ChemicalX - A PyTorch-based deep learning library for drug pair scoring. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • pytorch_geometric_temporal - Temporal Extension Library for PyTorch Geometric. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • pytorch-lightning - PyTorch Lightning is just organized PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • skorch - A scikit-learn compatible neural network library that wraps PyTorch. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

    • MXNet

      • Gluon - A clear, concise, simple yet powerful and efficient API for deep learning (now included in MXNet). <img height="20" src="img/mxnet_big.png" alt="MXNet based">
      • gluon-cv - Provides implementations of the state-of-the-art deep learning models in computer vision. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
      • gluon-nlp - NLP made easy. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
      • Xfer - Transfer Learning library for Deep Neural Networks. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
      • MXbox - Simple, efficient, and flexible vision toolbox for the mxnet framework. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
      • MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
      • MXNet - HIP Port of MXNet. <img height="20" src="img/mxnet_big.png" alt="MXNet based"> <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">

  • Model Explanation

    • NLP

      • themis-ml - A library that implements fairness-aware machine learning algorithms. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • FairML - FairML is a python toolbox auditing the machine learning models for bias. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • lucid - A collection of infrastructure and tools for research in neural network interpretability.
      • model-analysis - Model analysis tools for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • dalex - moDel Agnostic Language for Exploration and explanation. <img height="20" src="img/sklearn_big.png" alt="sklearn"><img height="20" src="img/R_big.png" alt="R inspired/ported lib">
      • scikit-plot - An intuitive library to add plotting functionality to scikit-learn objects. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Netron - Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).
      • Lime - Explaining the predictions of any machine learning classifier. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Shapley - A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
      • aequitas - Bias and Fairness Audit Toolkit.
      • Alibi - Algorithms for monitoring and explaining machine learning models.
      • PDPbox - Partial dependence plot toolbox.
      • anchor - Code for "High-Precision Model-Agnostic Explanations" paper.
      • mxboard - Logging MXNet data for visualization in TensorBoard. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
      • Contrastive Explanation - Contrastive Explanation (Foil Trees). <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • ELI5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions.
      • L2X - Code for replicating the experiments in the paper *Learning to Explain: An Information-Theoretic Perspective on Model Interpretation*.
      • treeinterpreter - Interpreting scikit-learn's decision tree and random forest predictions. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • PyCEbox - Python Individual Conditional Expectation Plot Toolbox.
      • shap - A unified approach to explain the output of any machine learning model. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Skater - Python Library for Model Interpretation.
      • AI Explainability 360 - Interpretability and explainability of data and machine learning models.
      • Auralisation - Auralisation of learned features in CNN (for audio).
      • CapsNet-Visualization - A visualization of the CapsNet layers to better understand how it works.
      • FlashLight - Visualization Tool for your NeuralNetwork.

  • Computer Vision

    • NLP

      • scikit-image - Image Processing SciKit (Toolbox for SciPy).
      • OpenCV - Open Source Computer Vision Library.
      • imgaug - Image augmentation for machine learning experiments.
      • Augmentor - Image augmentation library in Python for machine learning.
      • imgaug_extension - Additional augmentations for imgaug.

  • Computer Audition

    • NLP

      • librosa - Python library for audio and music analysis.
      • Essentia - Library for audio and music analysis, description, and synthesis.
      • madmom - Python audio and music signal processing library.
      • aubio - A library for audio and music analysis.
      • muda - A library for augmenting annotated audio data.
      • LibXtract - A simple, portable, lightweight library of audio feature extraction functions.
      • Yaafe - Audio features extraction.
      • Marsyas - Music Analysis, Retrieval, and Synthesis for Audio Signals.

  • Probabilistic Methods

    • NLP

      • PyMC - Bayesian Stochastic Modelling in Python.
      • GPyTorch - A highly efficient and modular implementation of Gaussian Processes in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • pomegranate - Probabilistic and graphical models for Python. <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
      • pgmpy - A python library for working with Probabilistic Graphical Models.
      • sklearn-bayes - Python package for Bayesian Machine Learning with scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • sklearn-crfsuite - A scikit-learn-inspired API for CRFsuite. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • PyVarInf - Bayesian Deep Learning methods with Variational Inference for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • MXFusion - Modular Probabilistic Programming on MXNet. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
      • pyhsmm - Bayesian inference in HSMMs and HMMs.
      • PyStan - Bayesian inference using the No-U-Turn sampler (Python interface).
      • emcee - The Python ensemble sampling toolkit for affine-invariant MCMC.
      • PtStat - Probabilistic Programming and Statistical Inference in PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • pyro - A flexible, scalable deep probabilistic programming library built on PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • ZhuSuan - Bayesian Deep Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • InferPy - Deep Probabilistic Modelling Made Easy. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • GPflow - Gaussian processes in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • skpro - Supervised domain-agnostic prediction framework for probabilistic modelling by [The Alan Turing Institute](https://www.turing.ac.uk/). <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • hsmmlearn - A library for hidden semi-Markov models with explicit durations.

  • Computations

    • NLP

      • NumExpr - A fast numerical expression evaluator for NumPy that comes with an integrated computing virtual machine to speed calculations up by avoiding memory allocation for intermediate results.
      • Dask - Parallel computing with task scheduling. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • CuPy - NumPy-like API accelerated with CUDA.
      • quaternion - Add built-in support for quaternions to numpy.
      • adaptive - Tools for adaptive and parallel samping of mathematical functions.
      • numdifftools - Solve automatic numerical differentiation problems in one or more variables.
      • bottleneck - Fast NumPy array functions written in C.
      • scikit-tensor - Python library for multilinear algebra and tensor factorizations.

  • Reinforcement Learning

    • NLP

      • keras-rl - Deep Reinforcement Learning for Keras. <img height="20" src="img/keras_big.png" alt="Keras compatible">
      • OpenAI Gym - A toolkit for developing and comparing reinforcement learning algorithms.
      • Dopamine - A research framework for fast prototyping of reinforcement learning algorithms.
      • garage - A toolkit for reproducible reinforcement learning research.
      • Stable Baselines - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
      • TF-Agents - A library for Reinforcement Learning in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • OpenAI Baselines - High-quality implementations of reinforcement learning algorithms.
      • ChainerRL - A deep reinforcement learning library built on top of Chainer.
      • TensorForce - A TensorFlow library for applied reinforcement learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • TRFL - TensorFlow Reinforcement Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • Coach - Easy experimentation with state-of-the-art Reinforcement Learning algorithms.
      • RLlib - Scalable Reinforcement Learning.
      • Horizon - A platform for Applied Reinforcement Learning.

  • Experimentation

    • NLP

      • mlflow - Open source platform for the machine learning lifecycle.
      • dvc - Data Version Control | Git for Data & Models | ML Experiments Management.
      • envd - 🏕️ machine learning development environment for data science and AI/ML engineering teams.
      • Ax - Adaptive Experimentation Platform. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Sacred - A tool to help you configure, organize, log, and reproduce experiments.

  • Feature Engineering

    • General

      • tsfresh - Automatic extraction of relevant features from time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Feature Engine - Feature engineering package with sklearn-like functionality. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • dirty_cat - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression). <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • NitroFE - Moving window features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Feature Forge - A set of tools for creating and testing machine learning features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • few - A feature engineering wrapper for sklearn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Featuretools - Automated feature engineering.
      • skl-groups - A scikit-learn addon to operate on set/"group"-based features. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • scikit-mdr - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. <img height="20" src="img/sklearn_big.png" alt="sklearn">

    • Feature Selection

      • scikit-feature - Feature selection repository in Python.
      • scikit-rebate - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • zoofs - A feature selection library based on evolutionary algorithms.
      • boruta_py - Implementations of the Boruta all-relevant feature selection method. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • BoostARoota - A fast xgboost feature selection algorithm. <img height="20" src="img/sklearn_big.png" alt="sklearn">

  • Statistics

    • NLP

      • statsmodels - Statistical modeling and econometrics in Python.
      • Alphalens - Performance analysis of predictive (alpha) stock factors.
      • scikit-posthocs - Pairwise Multiple Comparisons Post-hoc Tests.
      • stockstats - Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
      • weightedcalcs - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.

  • Conversion

    • NLP

      • ONNX - Open Neural Network Exchange.
      • sklearn-porter - Transpile trained scikit-learn estimators to C, Java, JavaScript, and others.
      • MMdnn - A set of tools to help users inter-operate among different deep learning frameworks.

  • Data Validation

    • NLP

      • evidently - Evaluate and monitor ML models from validation to production.
      • great_expectations - Always know what to expect from your data.
      • pandera - A lightweight, flexible, and expressive statistical data testing library.
      • deepchecks - Validation & testing of ML models and data during model development, deployment, and production. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • TensorFlow Data Validation - Library for exploring and validating machine learning data.

  • Spatial Analysis

    • NLP

      • PySal - Python Spatial Analysis Library.
      • GeoPandas - Python tools for geographic data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">

  • Distributed Computing

    • NLP

      • PaddlePaddle - PArallel Distributed Deep LEarning.
      • Distributed - Distributed computation in Python.
      • dask-ml - Distributed and parallel machine learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Jubatus - Framework and Library for Distributed Online Machine Learning.
      • Veles - Distributed machine learning platform.
      • DMTK - Microsoft Distributed Machine Learning Toolkit.
      • Horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • PySpark - Exposes the Spark programming model to Python. <img height="20" src="img/spark_big.png" alt="Apache Spark based">

  • Quantum Computing

    • NLP

      • cirq - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
      • qiskit - Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.
      • QML - A Python Toolkit for Quantum Machine Learning.
      • PennyLane - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.

  • Genetic Programming

    • NLP

      • DEAP - Distributed Evolutionary Algorithms in Python.
      • gplearn - Genetic Programming in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • monkeys - A strongly-typed genetic programming framework for Python.
      • sklearn-genetic - Genetic feature selection module for scikit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • karoo_gp - A Genetic Programming platform for Python with GPU support. <img height="20" src="img/tf_big2.png" alt="sklearn">

  • Evaluation

    • NLP

      • sklearn-evaluation - Model evaluation made easy: plots, tables, and markdown reports. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • Metrics - Machine learning evaluation metric.
      • recmetrics - Library of useful metrics and plots for evaluating recommender systems.
      • AI Fairness 360 - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.
Programming Languages
Python 190 Jupyter Notebook 22 C++ 21 JavaScript 3 C 2 TypeScript 2 HTML 1 Go 1 Rust 1
Categories
Machine Learning 45 Deep Learning 45 Data Manipulation 33 Model Explanation 26 Optimization 21 Probabilistic Methods 18 Visualization 16 Time Series 15 Feature Engineering 14 Reinforcement Learning 13 Computer Audition 8 Computations 8 Natural Language Processing 8 Distributed Computing 8 Genetic Programming 5 Data Validation 5 Experimentation 5 Computer Vision 5 Statistics 5 Quantum Computing 4 Deployment 4 Web Scraping 4 Evaluation 4 Conversion 3 Spatial Analysis 2 License 1
Sub Categories
NLP 169 TensorFlow 20 General Purpose Machine Learning 20 Data Frames 19 Others 12 Pipelines 10 PyTorch 10 General 9 MXNet 7 General Purposes 6 Kernel Methods 6 Ensemble Methods 5 Feature Selection 5 Interactive plots 5 Gradient Boosting 4 Data-centric AI 3 Automatic Plotting 3 Extreme Learning Machine 3 Automated Machine Learning 3 Imbalanced Datasets 2 Random Forests 2 Synthetic Data 1 Map 1
Keywords
machine-learning 100 python 87 deep-learning 47 data-science 42 pytorch 22 tensorflow 21 pandas 18 scikit-learn 17 keras 13 neural-network 13 ml 11 visualization 10 statistics 10 time-series 10 numpy 10 computer-vision 9 ai 9 mxnet 8 hyperparameter-optimization 8 optimization 8 data-analysis 8 forecasting 7 c-plus-plus 7 reinforcement-learning 7 nlp 7 gpu 7 natural-language-processing 7 data-visualization 7 dataframe 6 artificial-intelligence 6 automl 6 xgboost 6 interpretability 6 mlops 6 dask 6 distributed 6 cuda 6 neural-networks 6 automated-machine-learning 5 lightgbm 5 machinelearning 5 plotting 5 pandas-dataframe 5 anomaly-detection 4 jupyter 4 feature-engineering 4 feature-selection 4 data-validation 4 scipy 4 particle-swarm-optimization 4