AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics
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Updated
Oct 6, 2022 - HTML
AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics
Regularized discriminant analysis in Julia.
R package DiscriMiner
Machine Learning and Data Mining cheatsheet and example operations prepared in MATLAB
Function preserving projection (FPP), a linear projection technique for capturing interpretable patterns of high-dimensional functions
High Dimensional Discriminant Analysis in R ✨
This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.
[Built during technical internship at SAS Institute, May 2016 - Aug 2016] Created automated skin cancer detection software using image analysis, feature extraction, and statistical modeling that analyzes images of skin lesions to detect possibly cancerous growths. Presented research and algorithms at the international JMP Discovery Summit (also …
This repository includes the code for the paper "Detection of Prostate Cancer with Multi-Parametric MRI Utilizing the Anatomic Structure of the Prostate".
Linear Discriminant Analysis ~ a dimensionality reduction as well as a classification technique — with applications in document understanding
Breast cancer classification and evaluation of classifiers using k-fold cross-validation
Materiales de las clases prácticas de AID y Aprendizaje Automático
Case Study Based on Human Activity Recognition Using Smartphones Dataset
SYDE 372 - Lab 2
User segmentation using a sort of classifiers (some quite uncommon like the Fuzzy K-Nearest Neighbours).
Multi-distributional Discriminant Analysis using Generalised Linear Latent Variable Modelling in R ⭐
Performed statistical-EDA and normalization analysis on digitized mass images with 10 nuclei features (radius, texture) Predicted malignant - benign cancer using Logistic, LDA-QDA, KNN, Lasso-Ridge classifiers with 0.89, 0.88, 0.92, 0.96 and 0.97 accuracies respectively along with decision boundaries and ROC curves
Basic machine learning projects and algorithm implementation in Python3
Code developed for the Multivariate Statistics Spring 2019 course practice sessions.
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