ml经典机器学习算法的极简实现
Stars: ✭ 130 (+364.29%)
Machine-Learning-ModelsIn This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
Stars: ✭ 30 (+7.14%)
Handwritten-Digits-Classification-Using-KNN-Multiclass Perceptron-SVM🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.
Stars: ✭ 42 (+50%)
25daysinmachinelearningI will update this repository to learn Machine learning with python with statistics content and materials
Stars: ✭ 53 (+89.29%)
Jsmlt🏭 JavaScript Machine Learning Toolkit
Stars: ✭ 22 (-21.43%)
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!
Stars: ✭ 173 (+517.86%)
Machine Learning With PythonPractice and tutorial-style notebooks covering wide variety of machine learning techniques
Stars: ✭ 2,197 (+7746.43%)
Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-LearnFormed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…
Stars: ✭ 41 (+46.43%)
Dat8General Assembly's 2015 Data Science course in Washington, DC
Stars: ✭ 1,516 (+5314.29%)
supervised-machine-learningThis repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using…
Stars: ✭ 34 (+21.43%)
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
Stars: ✭ 160 (+471.43%)
Fuku MlSimple machine learning library / 簡單易用的機器學習套件
Stars: ✭ 280 (+900%)
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.
Stars: ✭ 5,656 (+20100%)
MachineLearning机器学习教程,本教程包含基于numpy、sklearn与tensorflow机器学习,也会包含利用spark、flink加快模型训练等用法。本着能够较全的引导读者入门机器学习。
Stars: ✭ 23 (-17.86%)
SentimentAnalysis(BOW, TF-IDF, Word2Vec, BERT) Word Embeddings + (SVM, Naive Bayes, Decision Tree, Random Forest) Base Classifiers + Pre-trained BERT on Tensorflow Hub + 1-D CNN and Bi-Directional LSTM on IMDB Movie Reviews Dataset
Stars: ✭ 40 (+42.86%)
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
Stars: ✭ 395 (+1310.71%)
SporfThis is the implementation of Sparse Projection Oblique Randomer Forest
Stars: ✭ 70 (+150%)
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)
Stars: ✭ 78 (+178.57%)
MachinelearnjsMachine Learning library for the web and Node.
Stars: ✭ 498 (+1678.57%)
Ml ProjectsML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
Stars: ✭ 127 (+353.57%)
biovecProtVec can be used in protein interaction predictions, structure prediction, and protein data visualization.
Stars: ✭ 23 (-17.86%)
Machine Learning In RWorkshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
Stars: ✭ 144 (+414.29%)
Isl PythonSolutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
Stars: ✭ 108 (+285.71%)
ChefboostA Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python
Stars: ✭ 176 (+528.57%)
Tensorflow Ml Nlp텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)
Stars: ✭ 176 (+528.57%)
Orange3🍊 📊 💡 Orange: Interactive data analysis
Stars: ✭ 3,152 (+11157.14%)
Statistical-Learning-using-RThis is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
Stars: ✭ 27 (-3.57%)
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 (-25%)
GDLibraryMatlab library for gradient descent algorithms: Version 1.0.1
Stars: ✭ 50 (+78.57%)
VisualMLInteractive Visual Machine Learning Demos.
Stars: ✭ 104 (+271.43%)
goscoreGo Scoring API for PMML
Stars: ✭ 85 (+203.57%)
rfvisA tool for visualizing the structure and performance of Random Forests 🌳
Stars: ✭ 20 (-28.57%)
yggdrasil-decision-forestsA collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.
Stars: ✭ 156 (+457.14%)
scorubyRuby Scoring API for PMML
Stars: ✭ 69 (+146.43%)
handson-ml도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
Stars: ✭ 285 (+917.86%)
info-retrievalInformation Retrieval in High Dimensional Data (class deliverables)
Stars: ✭ 33 (+17.86%)
ICC-2019-WC-predictionPredicting the winner of 2019 cricket world cup using random forest algorithm
Stars: ✭ 41 (+46.43%)