Model Describermodel-describer : Making machine learning interpretable to humans
Stars: ✭ 22 (+4.76%)
PracticalMachineLearningA collection of ML related stuff including notebooks, codes and a curated list of various useful resources such as books and softwares. Almost everything mentioned here is free (as speech not free food) or open-source.
Stars: ✭ 60 (+185.71%)
kenchiA scikit-learn compatible library for anomaly detection
Stars: ✭ 36 (+71.43%)
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.
Stars: ✭ 218 (+938.1%)
SktimeA unified framework for machine learning with time series
Stars: ✭ 4,741 (+22476.19%)
TextClassification基于scikit-learn实现对新浪新闻的文本分类,数据集为100w篇文档,总计10类,测试集与训练集1:1划分。分类算法采用SVM和Bayes,其中Bayes作为baseline。
Stars: ✭ 86 (+309.52%)
Orange3🍊 📊 💡 Orange: Interactive data analysis
Stars: ✭ 3,152 (+14909.52%)
imbalanced-ensembleClass-imbalanced / Long-tailed ensemble learning in Python. Modular, flexible, and extensible. | 模块化、灵活、易扩展的类别不平衡/长尾机器学习库
Stars: ✭ 199 (+847.62%)
dlime experimentsIn this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
Stars: ✭ 21 (+0%)
Medium-Stats-AnalysisExploring data and analyzing metrics for user-specific Medium Stats
Stars: ✭ 27 (+28.57%)
scikit-learn-moocMachine learning in Python with scikit-learn MOOC
Stars: ✭ 783 (+3628.57%)
ML-TrackThis repository is a recommended track, designed to get started with Machine Learning.
Stars: ✭ 19 (-9.52%)
audio noise clusteringhttps://dodiku.github.io/audio_noise_clustering/results/ ==> An experiment with a variety of clustering (and clustering-like) techniques to reduce noise on an audio speech recording.
Stars: ✭ 24 (+14.29%)
nlp workshop odsc europe20Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and T…
Stars: ✭ 127 (+504.76%)
pycobrapython library implementing ensemble methods for regression, classification and visualisation tools including Voronoi tesselations.
Stars: ✭ 111 (+428.57%)
AutoTabularAutomatic machine learning for tabular data. ⚡🔥⚡
Stars: ✭ 51 (+142.86%)
mlhandbookMy textbook for teaching Machine Learning
Stars: ✭ 23 (+9.52%)
simon-frontend💹 SIMON is powerful, flexible, open-source and easy to use machine learning knowledge discovery platform 💻
Stars: ✭ 114 (+442.86%)
xgboost-smote-detect-fraudCan we predict accurately on the skewed data? What are the sampling techniques that can be used. Which models/techniques can be used in this scenario? Find the answers in this code pattern!
Stars: ✭ 59 (+180.95%)
sklearn-pmml-modelA library to parse and convert PMML models into Scikit-learn estimators.
Stars: ✭ 71 (+238.1%)
iisInformation Inference Service of the OpenAIRE system
Stars: ✭ 16 (-23.81%)
kaggledatasetsCollection of Kaggle Datasets ready to use for Everyone (Looking for contributors)
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Voice4RuralA complete one stop solution for all the problems of Rural area people. 👩🏻🌾
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bsu🎓Repository for university labs on FAMCS, BSU
Stars: ✭ 91 (+333.33%)
scibloxsciblox - Easier Data Science and Machine Learning
Stars: ✭ 48 (+128.57%)
interpretable-mlTechniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Stars: ✭ 17 (-19.05%)
KaliIntelligenceSuiteKali Intelligence Suite (KIS) shall aid in the fast, autonomous, central, and comprehensive collection of intelligence by executing standard penetration testing tools. The collected data is internally stored in a structured manner to allow the fast identification and visualisation of the collected information.
Stars: ✭ 58 (+176.19%)
verbeccComplete Conjugation of any Verb using Machine Learning for French, Spanish, Portuguese, Italian and Romanian
Stars: ✭ 45 (+114.29%)
hierarchical-clusteringA Python implementation of divisive and hierarchical clustering algorithms. The algorithms were tested on the Human Gene DNA Sequence dataset and dendrograms were plotted.
Stars: ✭ 62 (+195.24%)
CubistA Python package for fitting Quinlan's Cubist regression model
Stars: ✭ 22 (+4.76%)
ml-restREST API (and possible UI) for Machine Learning workflows
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Data-Analyst-NanodegreeThis repo consists of the projects that I completed as a part of the Udacity's Data Analyst Nanodegree's curriculum.
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skrobotskrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.
Stars: ✭ 22 (+4.76%)
PyDREAMPython Implementation of Decay Replay Mining (DREAM)
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conferencias matutinas amloCSVs de las versiones estenográficas de las conferencias matutinas del Presidente Andres Manuel López Obrador ( Mañaneras AMLO )
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website-to-jsonConverts website to json using jQuery selectors
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rezonanceContent Based Music Recommendation Service
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dh-coreFunctional data science
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PySPODA Python package for spectral proper orthogonal decomposition (SPOD).
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MetQyRepository for R package MetQy (read related publication here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247936/)
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website-fingerprintingDeanonymizing Tor or VPN users with website fingerprinting and machine learning.
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cliPolyaxon Core Client & CLI to streamline MLOps
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ml webappExplore machine learning models. Leveraging scikit-learn's models and exposing their behaviour through API
Stars: ✭ 29 (+38.1%)
data-miningResources for the Data Mining for Bussiness and Governance course.
Stars: ✭ 52 (+147.62%)
Apriori-and-Eclat-Frequent-Itemset-MiningImplementation of the Apriori and Eclat algorithms, two of the best-known basic algorithms for mining frequent item sets in a set of transactions, implementation in Python.
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