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.

Classification base on kernel SVM

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

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.

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

cReddit: Misinformation Assessment Tool for Comments from Reddit

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

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.

In this project the data is been used from UCI Machinery Repository. Main aim of this project is to predict telling tumor of each patient is Benign (class – 2) or Malignant (class – 4) the models used are – Decision tree Classification, Logistic Regression, K-Nearest Neighbors, SVM, Kernel SVM, Naïve-Bayes and Random Forest Classification.