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

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.

Classification base on kernel SVM

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.

Label classification for three datasets: Face, Pose and Illumination. Bayes Classifier, KNN Classier, Kerner SVM and Boosted SVM algorithms are written from scratch in Python. The results were evaluated and compared to understand the effectr of dimentionality reduction techniques including PCA, LDA and MDA validation using K-fold cross validation.

Handwritten digits recognition using logistic regression, Linear with PCA and LDA or dimensionality reduction and Kernel SVM, and Lenet-5 .

cReddit: Misinformation Assessment Tool for Comments from Reddit

Complete Tutorial Guide with Code for learning ML

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.

In this project, I compare several commonly used machine learning models, namely K-Nearest Neighbors (KNN), Kernel SVM, Logistic Regression, Naive Bayes, SVM, Decision Tree, and Random Forest. I evaluate and compare the performance and accuracy of these models using a breast cancer dataset, and get the confusion matrix and accuracy score.