Machine Learning Is All You Need🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
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Machine Learning ModelsDecision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
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EmlearnMachine Learning inference engine for Microcontrollers and Embedded devices
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Machine Learning With PythonPractice and tutorial-style notebooks covering wide variety of machine learning techniques
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Benchm MlA minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
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Machine Learning In RWorkshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
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Awesome Decision Tree PapersA collection of research papers on decision, classification and regression trees with implementations.
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Ml ProjectsML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
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Isl PythonSolutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
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Predicting real estate prices using scikit LearnPredicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
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GcforestThis is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'
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SporfThis is the implementation of Sparse Projection Oblique Randomer Forest
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RoffildlibraryLibrary for MQL5 (MetaTrader) with Python, Java, Apache Spark, AWS
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Edarfexploratory data analysis using random forests
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Stock Market Sentiment AnalysisIdentification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka
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25daysinmachinelearningI will update this repository to learn Machine learning with python with statistics content and materials
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TpotA Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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Cnn Svm ClassifierUsing Tensorflow and a Support Vector Machine to Create an Image Classifications Engine
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Mljar SupervisedAutomated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀
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Jsmlt🏭 JavaScript Machine Learning Toolkit
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Grtgesture recognition toolkit
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H2o 3H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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ThundergbmThunderGBM: Fast GBDTs and Random Forests on GPUs
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Deep ForestAn Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)
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GrfGeneralized Random Forests
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MachinelearnjsMachine Learning library for the web and Node.
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Pytorch classification利用pytorch实现图像分类的一个完整的代码,训练,预测,TTA,模型融合,模型部署,cnn提取特征,svm或者随机森林等进行分类,模型蒸馏,一个完整的代码
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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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Rrcf🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
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DtreevizA python library for decision tree visualization and model interpretation.
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MachineLearningSeriesVídeos e códigos do Universo Discreto ensinando o fundamental de Machine Learning em Python. Para mais detalhes, acompanhar a playlist listada.
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2020plusClassifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests
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linear-treeA python library to build Model Trees with Linear Models at the leaves.
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GeFsGenerative Forests in Python
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AdaptiveRandomForestRepository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04
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EurekaTreesVisualizes the Random Forest debug string from the MLLib in Spark using D3.js
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urb-studies-predicting-gentrificationThis repo is intended to support replication and exploration of the analysis undertaken for our Urban Studies article "Understanding urban gentrification through Machine Learning: Predicting neighbourhood change in London".
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impute-meThis is the code behind the www.impute.me site. It contains algorithms for personal genome analysis, including imputation and polygenic risk score calculation
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imputationserverMichigan Imputation Server: A new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity
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simputationMaking imputation easy
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