Learning to create Machine Learning Algorithms

Breast Cancer Wisconsin (Diagnostic) Prediction Using Various Architecture, though XgBoost Classifier out performed all

Implementation of the Gaussian RBF Kernel in Support Vector Machine model.

All my Machine Learning Projects from A to Z in (Python & R)

Numpy based implementation of kernel based SVM

Time Series Analyses and Machine Learning for Classifying Events prior to Fiber Cuts

Classification base on kernel SVM

Package provides javascript implementation of support vector machines

Contains ML Algorithms implemented as part of CSE 512 - Machine Learning class taken by Fransico Orabona. Implemented Linear Regression using polynomial basis functions, Perceptron, Ridge Regression, SVM Primal, Kernel Ridge Regression, Kernel SVM, Kmeans.

Face recognition using various classifiers

Full machine learning practical with Python.

Machine learning course at Tel-Aviv University, 2016

Full machine learning practical with R.

cReddit: Misinformation Assessment Tool for Comments from Reddit

Classifying purchase events with introduction of dimensions to linearly separate the data points. The SVM algorithm uses Radial basis Function (RBF) Kernel.

in this repository i am going to perform kernel SVM Classifcation on the real life dataset , initially i performed some data preprocessing technique in order to filter out the data flaws then undergoes the process of model building i.e Kernel SVM Classification.

We consider a problem of minimizing a sum of two functions and propose a generic algorithmic framework (SAE) to separate oracle complexities for each function. We compare the performance of splitting accelerated enveloped accelerated variance reduced method with a different sliding technique.

working with some of basic and advance machine learning in scikit-learn

Cross-validation, knn classif, knn régression, svm à noyau, Ridge à noyau

Implementation of some Machine Learning Algorithms in Python