Popular GitHub Repositories for Discriminant Anlaysis

POT : Python Optimal Transport

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract)

Data Science algorithms and topics that you must know. (Newly Designed) Recommender Systems, Decision Trees, K-Means, LDA, RFM-Segmentation, XGBoost in Python, R, and Scala.

Basic Machine Learning implementation with python

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

In depth machine learning resources

Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Image Classification with Perceptron and LDA and PCA dimension reduction

This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.

Automated polysomnography for experimental animal research

Insurance claim fraud detection using machine learning algorithms.

BrainVision EEG data classification using the MNE, Keras and the scikit-learn libraries.

Face Recognition with SVM classifier using PCA, ICA, NMF, LDA reduced face vectors

Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning

Linear discriminant Analysis(LDA) for Wine Dataset of Machine Learning

This project utilizes Linear Discriminant Analysis (LDA) to address high dimensionality in a white wine dataset. LDA aims to reduce feature complexity, aiding visualization and efficient model training. The project aims to uncover factors contributing to high-quality white wine production by classifying wine quality based on reduced features.

The code for Quantized Fisher Discriminant Analysis (QFDA)

Statistical Machine Learning

Comprehensive Object-Oriented Programming Python implementation of a machine learning pipeline for diabetes prediction, featuring nested cross-validation, Bayesian hyperparameter optimization, and robust preprocessing for accurate and reliable outcomes.