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.

Full machine learning practical with Python.

Face recognition using various classifiers

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.

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.

Complete Tutorial Guide with Code for learning ML

cReddit: Misinformation Assessment Tool for Comments from Reddit

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

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

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.

Implementation of some Machine Learning Algorithms in Python

Machine Learning Mastery is a comprehensive repository designed to teach machine learning with Python. It covers essential techniques from data preprocessing to advanced methods in classification, regression, and clustering, catering to beginners and advanced learners alike.

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

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.

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

No description provided.

Face recognition with Bayesian Classifier, KNN, KernelSVM (Linear, RBF, Polynomial), Boosted SVM, PCA, LDA

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.

Exploration and optimization of a ML pipeline, delving into various techniques for enhancing different stages of ML workflows, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.

Support Vector Machine

Trained and compared multiple ML models on a Kaggle thyroid cancer dataset. Tested class balancing and PCA to see how preprocessing affects each model.