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awesome-python-data-science

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

Last synced: 7 days ago
JSON representation

  • Computations

    • NLP

      • Dask - Parallel computing with task scheduling. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • CuPy - NumPy-like API accelerated with CUDA.
      • scikit-tensor - Python library for multilinear algebra and tensor factorizations.
      • numdifftools - Solve automatic numerical differentiation problems in one or more variables.
      • quaternion - Add built-in support for quaternions to numpy.
      • adaptive - Tools for adaptive and parallel samping of mathematical functions.
      • 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.
      • numpy - The fundamental package needed for scientific computing with Python.

  • Computer Audition

    • NLP

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

  • Computer Vision

    • NLP

      • OpenCV - Open Source Computer Vision Library.
      • scikit-image - Image Processing SciKit (Toolbox for SciPy).
      • imgaug - Image augmentation for machine learning experiments.
      • Augmentor - Image augmentation library in Python for machine learning.
      • imgaug_extension - Additional augmentations for imgaug.
      • albumentations - Fast image augmentation library and easy-to-use wrapper around other libraries.

  • Conversion

    • NLP

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

  • Data Manipulation

    • Data-centric AI

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

    • Data Frames

      • pandas - Powerful Python data analysis toolkit.
      • 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">
      • blaze - NumPy and pandas interface to Big Data. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • pandasql - Allows you to query pandas DataFrames using SQL syntax. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • xpandas - Universal 1d/2d data containers with Transformers .functionality for data analysis by [The Alan Turing Institute](https://www.turing.ac.uk/).
      • 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">
      • Arctic - High-performance datastore for time series and tick data.
      • datatable - Data.table for Python. <img height="20" src="img/R_big.png" alt="R inspired/ported lib">
      • koalas - pandas API on Apache Spark. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • 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.
      • pandas_flavor - A package that allows writing your own flavor of Pandas easily.
      • pandas-log - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
      • vaex - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
      • 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.
      • 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">
      • polars - A fast multi-threaded, hybrid-out-of-core DataFrame library.
      • pandas-gbq - pandas Google Big Query. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
      • pandas_profiling - Create HTML profiling reports from pandas DataFrame objects

    • Pipelines

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

    • 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">

  • Data Validation

    • NLP

      • 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">
      • evidently - Evaluate and monitor ML models from validation to production.
      • TensorFlow Data Validation - Library for exploring and validating machine learning data.

  • Deep Learning

    • MXNet

      • 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">
      • 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">
      • MXbox - Simple, efficient, and flexible vision toolbox for the mxnet framework. <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">
      • 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">

    • Others

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

    • PyTorch

      • PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
      • torchvision - Datasets, Transforms, and Models specific to Computer Vision. <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">
      • pytorch_geometric_temporal - Temporal Extension Library for PyTorch Geometric. <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 - Geometric Deep Learning Extension Library for PyTorch. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">

    • TensorFlow

      • TensorFlow - Computation using data flow graphs for scalable machine learning by Google. <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">
      • 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">
      • Polyaxon - A platform that helps you build, manage and monitor deep learning models. <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">
      • 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-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">
      • 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">
      • Mesh TensorFlow - Model Parallelism Made Easier. <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">
      • Elephas - Distributed Deep learning with Keras & Spark. <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">
      • qkeras - A quantization deep learning library. <img height="20" src="img/keras_big.png" alt="Keras compatible">
      • TensorLight - A high-level framework for TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn">
      • 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">

  • 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.

  • Distributed Computing

    • NLP

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

  • Evaluation

    • NLP

      • recmetrics - Library of useful metrics and plots for evaluating recommender systems.
      • Metrics - Machine learning evaluation metric.
      • sklearn-evaluation - Model evaluation made easy: plots, tables, and markdown reports. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • AI Fairness 360 - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.

  • Experimentation

    • NLP

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

  • Feature Engineering

    • Feature Selection

      • scikit-feature - Feature selection repository in Python.
      • 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">
      • 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.

    • General

      • Featuretools - Automated feature engineering.
      • Feature Engine - Feature engineering package with sklearn-like functionality. <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">
      • 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">
      • tsfresh - Automatic extraction of relevant features from time series. <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">

  • Genetic Programming

    • NLP

      • gplearn - Genetic Programming in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • DEAP - Distributed Evolutionary Algorithms in Python.
      • karoo_gp - A Genetic Programming platform for Python with GPU support. <img height="20" src="img/tf_big2.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">

  • Machine Learning

    • 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.
      • TPOT - Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. <img height="20" src="img/sklearn_big.png" alt="sklearn">

    • Ensemble Methods

      • ML-Ensemble - High performance ensemble learning. <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">
      • Stacking - Simple and useful stacking library written in Python. <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">

    • 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">

    • General Purpose Machine Learning

      • Shogun - Machine learning toolbox.
      • scikit-learn - Machine learning in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • xLearn - High Performance, Easy-to-use, and Scalable Machine Learning Package.
      • 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">
      • 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">
      • mlpack - A scalable C++ machine learning library (Python bindings).
      • dlib - Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).
      • MLxtend - Extension and helper modules for Python's data analysis and machine learning libraries. <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">
      • Reproducible Experiment Platform (REP) - Machine Learning toolbox for Humans. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • scikit-multilearn - Multi-label classification for python. <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">
      • sklearn-expertsys - Highly interpretable classifiers for scikit learn. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • RuleFit - Implementation of the rulefit. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • pyGAM - Generalized Additive Models in Python.
      • Karate Club - An unsupervised machine learning library for graph-structured data.
      • Little Ball of Fur - A library for sampling graph structured data.
      • causalml - Uplift modeling and causal inference with machine learning algorithms. <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

      • 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">
      • 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">
      • 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">
      • 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">

    • 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

      • 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">
      • liquidSVM - An implementation of SVMs.
      • scikit-rvm - Relevance Vector Machine implementation using the scikit-learn API. <img height="20" src="img/sklearn_big.png" alt="sklearn">
      • 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">

    • 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">
      • rgf_python - Python Wrapper of Regularized Greedy Forest. <img height="20" src="img/sklearn_big.png" alt="sklearn">

  • Model Explanation

    • NLP

      • 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">
      • Shapley - A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
      • Alibi - Algorithms for monitoring and explaining machine learning models.
      • anchor - Code for "High-Precision Model-Agnostic Explanations" paper.
      • aequitas - Bias and Fairness Audit Toolkit.
      • Contrastive Explanation - Contrastive Explanation (Foil Trees). <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">
      • scikit-plot - An intuitive library to add plotting functionality to scikit-learn objects. <img height="20" src="img/sklearn_big.png" alt="sklearn">
Programming Languages
Python 197 C++ 22 Jupyter Notebook 21 JavaScript 3 C 2 TypeScript 2 HTML 1 Go 1 Rust 1
Categories
Machine Learning 47 Deep Learning 43 Data Manipulation 33 Model Explanation 26 Optimization 20 Visualization 17 Probabilistic Methods 16 Time Series 15 Feature Engineering 13 Reinforcement Learning 13 Computer Audition 8 Computations 8 Natural Language Processing 8 Distributed Computing 7 Experimentation 6 Computer Vision 6 Statistics 5 Data Validation 5 Genetic Programming 5 Web Scraping 4 Evaluation 4 Deployment 3 Quantum Computing 3 Conversion 3 Spatial Analysis 2 License 1
Sub Categories
NLP 165 General Purpose Machine Learning 20 TensorFlow 19 Data Frames 19 Others 12 Pipelines 10 PyTorch 9 General 8 MXNet 7 General Purposes 6 Kernel Methods 6 Ensemble Methods 5 Feature Selection 5 Interactive plots 5 Gradient Boosting 4 Automated Machine Learning 4 Data-centric AI 3 Automatic Plotting 3 Extreme Learning Machine 3 Random Forests 3 Imbalanced Datasets 2 Map 2 Synthetic Data 1
Keywords
machine-learning 99 python 88 deep-learning 44 data-science 40 tensorflow 23 pytorch 20 pandas 18 scikit-learn 17 neural-network 13 keras 13 visualization 11 time-series 10 ml 10 statistics 10 numpy 10 hyperparameter-optimization 9 optimization 9 ai 8 mxnet 8 computer-vision 8 data-analysis 8 forecasting 7 nlp 7 xgboost 7 reinforcement-learning 7 data-visualization 7 gpu 7 c-plus-plus 7 dask 6 distributed 6 cuda 6 mlops 6 natural-language-processing 6 neural-networks 6 automl 6 dataframe 6 pandas-dataframe 5 lightgbm 5 machinelearning 5 artificial-intelligence 5 plotting 5 automated-machine-learning 5 feature-selection 5 machine-learning-algorithms 5 interpretability 5 jupyter 4 particle-swarm-optimization 4 pydata 4 gbdt 4 bayesian-optimization 4