280 Open source projects that are alternatives of or similar to introduction-to-machine-learning

Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review

simple tutorial on Machine Learning with Scikitlearn

In 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).

经典机器学习算法的极简实现

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

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

🏭 JavaScript Machine Learning Toolkit

Formed 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…

Implementation of models in our EMNLP 2019 paper: A Logic-Driven Framework for Consistency of Neural Models

FUKinect-Fall dataset was created using Kinect V1. The dataset includes walking, bending, sitting, squatting, lying and falling actions performed by 21 subjects between 19-72 years of age.

My solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

Python code for common Machine Learning Algorithms

⚡机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

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。

Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

🏆 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.

(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

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Machine Learning library for the web and Node.

Master's degree project: Development of a trading algorithm which uses supervised machine learning classification techniques to generate buy/sell signals

文本分类基准测试

ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python

Software for learning sparse Bayesian networks

Animated Visualizations of Popular Machine Learning Algorithms

This 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.

This is the official implementation for the paper 'AutoEncoder by Forest'

Scripts, tools and example data for mapping wetland ecosystems using data driven R statistical methods like Random Forests and open source GIS

A practice project for machine learning and stop price prediction

A super-fast and scalable Random Forest library based on fast histogram decision tree algorithm and distributed bagging framework. It can be used for binary classification, multi-label classification, and regression tasks. This library provides both Python and command line interface to users.

Efficient Algorithms for L0 Regularized Learning

The R package for SVM with GPU architecture based on the GTSVM software

A random forest

Go Scoring API for PMML

Machine Learning library written in Python and NumPy.

Improving MMD-GAN training with repulsive loss function

Hyperstar: Negative Sampling Improves Hypernymy Extraction Based on Projection Learning.

Heart Disease prediction using 5 algorithms

整理记录本人担任课程助教设计的四个机器学习实验，主要涉及简单的线性回归、朴素贝叶斯分类器、支持向量机、CNN做文本分类。内附实验指导书、讲解PPT、参考代码，欢迎各位码友讨论交流。

Comparison of active site and full kinase sequences for drug-target affinity prediction and molecular generation. Full paper: https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889

LIBSVM bindings for Julia

OCR Machine Learning in python

Implementing Content based and Collaborative filtering(with KNN, Matrix Factorization and Neural Networks) in Python

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

A SVM model that classifies the reviews as real or fake. Used both the review text and the additional features contained in the data set to build a model that predicted with over 85% accuracy without using any deep learning techniques.

Conformalized Quantile Regression

Automatic license plate recognition for Indonesian plate (White on black)

DataFrame support for scikit-learn.

This repository is a recommended track, designed to get started with Machine Learning.

liblinear bindings for Go

(Pytorch and Tensorflow) Implementation of Weighted Contrastive Loss (Deep Metric Learning by Online Soft Mining and Class-Aware Attention)

The implementation of 3D-UNet using PyTorch

This is the code for Addressing Class Imbalance in Federated Learning (AAAI-2021).

Julia package of regularization functions for machine learning

This project is to convert PortraitFCN+ (by Xiaoyong Shen) from Matlab to Tensorflow, then refine the outputs from it (converted to a trimap) using KNN and ResNet, supervised by Richard Berwick.

Highly efficient PyTorch version of the Semi-hard Triplet loss ⚡️

Utilities for glmnet

Computationally efficient confidence intervals for cross-validated AUC estimates in R

library of numerical methods using Armadillo

ML-powered Loan-Marketer Customer Filtering Engine