Plain python implementations of basic machine learning algorithms
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Updated
May 21, 2024 - Jupyter Notebook
Plain python implementations of basic machine learning algorithms
implement the machine learning algorithms by python for studying
Data Mining Algorithms with C# using LINQ
Interactive K-Nearest Neighbors machine learning algorithm in JavaScript.
Calibration of an air pollution sensor monitoring network in uncontrolled environments with multiple machine learning algorithms
Adversarial Examples on KNN (and its neural network friends)
This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.
An implementation of the K-Nearest Neighbors algorithm from scratch using the Python programming language.
Deep Learning breast histology microscopy image recognition using Convolutional Neural Networks
Check out the projects that I have made using scikit-learn.
This module introduces classification — you will be implementing the various techniques such as k-nearest neighbors and Support Vector Machines. You will be using the Euclidean distance to work with the k-nearest neighbors.
Heart Disease Classification with Python
A Python Machine Learning classification task to predict fall incidents in elderly persons taking into account reports and clinical information. The prediction application is live and usable on Streamlit to predict the possibility of falls in elderly persons.
Generic RTree for Nim
This repo was created to share the source code for the initial paper about automatic identification of interlanguage transfer phenomena between Brazilian Portuguese and American English using machine learning techniques.
scikit-learn compatible estimators for various kNN imputation methods
📌 1. Compute the Mahalanobis distance from a centroid for a given set of training points. 2. Implement Radial Basis function (RBF) Gaussian Kernel Perceptron. 3. Implement a k-nearest neighbor (kNN) classifier
IBM Project
Using SciKit Learn few Deep Learning Rules and Algorithms are implemented
Credit Card Fraud Detection using KNN and K-means
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