100 Days of ML Coding

Software designed to identify and monitor social/historical cues for short term stock movement

Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera

Text Classification Algorithms: A Survey

Simple machine learning library / 簡單易用的機器學習套件

Machine Learning Lectures at the European Space Agency (ESA) in 2018

A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.

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) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz

Insanely fast computer vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)

A vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM).

A MATLAB toolbox for classifier: Version 1.0.7

Geolocating twitter users by the content of their tweets

Python Machine Learning Algorithms

A C++ toolkit for Convex Optimization (Logistic Loss, SVM, SVR, Least Squares etc.), Convex Optimization algorithms (LBFGS, TRON, SGD, AdsGrad, CG, Nesterov etc.) and Classifiers/Regressors (Logistic Regression, SVMs, Least Squares Regression etc.)

Predicting the winner of 2019 cricket world cup using random forest algorithm

This is the page for the book Digital Signal Processing with Kernel Methods.

Statistical inference on machine learning or general non-parametric models

implement the machine learning algorithms by python for studying

The Fashion-MNIST dataset and machine learning models.

MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3

Tutorial: Support Vector Machine from scratch using Python3

Spam filtering module with Machine Learning using SVM (Support Vector Machines).

🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

Misc Statistics and Machine Learning codes in R

Implementation of a paper in q/KDB+ and python - "Forecasting ETFs with Machine Learning Algorithms" - Jim Kyung-Soo Liew and Boris Mayster

Today, using machine learning algorithms is as easy as "import knn from ..." but it doesn't really help if you want to learn how the algorithms work

Predicting emotions based on speech audio samples of American English, German and British English languages using Support Vector Machine, K-Nearest Neighbor, Random Forest and Recurrent Neural Network. Analyzing the performance of each model based on the dataset.

A comprehensive approach on recognizing emotion (sentiment) from a certain tweet. Supervised machine learning.