Open source demosA collection of demos showcasing automated feature engineering and machine learning in diverse use cases
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Amazing Feature EngineeringFeature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
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DeltapyDeltaPy - Tabular Data Augmentation (by @firmai)
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Kaggle CompetitionsThere are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
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Datasist A Python library for easy data analysis, visualization, exploration and modeling
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Data ScienceCollection of useful data science topics along with code and articles
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Agile data code 2Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition
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Edward2A simple probabilistic programming language.
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FeaturetoolsAn open source python library for automated feature engineering
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EvidentlyInteractive reports to analyze machine learning models during validation or production monitoring.
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ProbabilityProbabilistic reasoning and statistical analysis in TensorFlow
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Quantitative NotebooksEducational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
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TsfreshAutomatic extraction of relevant features from time series:
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Stats Maths With PythonGeneral statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
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Python Causality HandbookCausal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.
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CoursesQuiz & Assignment of Coursera
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EdwardA probabilistic programming language in TensorFlow. Deep generative models, variational inference.
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Data Science Your WayWays of doing Data Science Engineering and Machine Learning in R and Python
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Course V3The 3rd edition of course.fast.ai
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Feature SelectionFeatures selector based on the self selected-algorithm, loss function and validation method
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Apricotapricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html
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ThesemicolonThis repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
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D2l PytorchThis project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
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CartolaExtração de dados da API do CartolaFC, análise exploratória dos dados e modelos preditivos em R e Python - 2014-20. [EN] Data munging, analysis and modeling of CartolaFC - the most popular fantasy football game in Brazil and maybe in the world. Data cover years 2014-19.
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User Machine Learning TutorialuseR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
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Hyperparameter hunterEasy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
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Production Data ScienceProduction Data Science: a workflow for collaborative data science aimed at production
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Jupyter pivottablejsDrag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook, care of PivotTable.js
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Mli ResourcesH2O.ai Machine Learning Interpretability Resources
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Awesome Feature EngineeringA curated list of resources dedicated to Feature Engineering Techniques for Machine Learning
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Nteract📘 The interactive computing suite for you! ✨
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Tensor HouseA collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain
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PbaEfficient Learning of Augmentation Policy Schedules
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Food Recipe Cnnfood image to recipe with deep convolutional neural networks.
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Interpretable machine learning with pythonExamples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
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Intro To PythonAn intro to Python & programming for wanna-be data scientists
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Python Ml CourseCurso de Introducción a Machine Learning con Python
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Zero To Mastery MlAll course materials for the Zero to Mastery Machine Learning and Data Science course.
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Data Science PortfolioPortfolio of data science projects completed by me for academic, self learning, and hobby purposes.
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Sigma coding youtubeThis is a collection of all the code that can be found on my YouTube channel Sigma Coding.
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Speech Emotion AnalyzerThe neural network model is capable of detecting five different male/female emotions from audio speeches. (Deep Learning, NLP, Python)
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Fastai2Temporary home for fastai v2 while it's being developed
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Pydataroadopen source for wechat-official-account (ID: PyDataLab)
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Python SeminarPython for Data Science (Seminar Course at UC Berkeley; AY 250)
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