Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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

👑 Python factor analysis library (PCA, CA, MCA, MFA, FAMD)

The foundational library of the Morpheus data science framework

Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

quizzes/assignments for mathematics for machine learning specialization on coursera

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].

Robust PCA implementation and examples (Matlab)

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

Fast truncated singular value decompositions

Randomized Dimension Reduction Library

UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.

Randomized Matrix Decompositions using R

Do you look like a Nobel Laureate 🎖️, Physicist, Chemist, Mathematician, Actor or a Programmer? God gave you one face and you went on to get a peek into the mind of God. 🌩️

A MATLAB toolbox for classifier: Version 1.0.7

An approach to document exploration using Machine Learning. Let's cluster similar research articles together to make it easier for health professionals to find relevant research articles, and respond to rapidly spreading COVID-19 promptly.

The goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.

Implementation of Machine Learning Algorithms

Explorative multivariate statistics in Python

✍️ An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.

Machine Learning Library, written in J

A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).

Information Retrieval in High Dimensional Data (class deliverables)

implement the machine learning algorithms by python for studying

All codes, both created and optimized for best results from the SuperDataScience Course

Principal component analysis (PCA) in Ruby

Misc Statistics and Machine Learning codes in R

Python library to handle Scanning Probe Microscopy Images. Can read nanoscan .xml data, Bruker AFM images, Nanonis SXM files as well as iontof images(ITA, ITM and ITS).

Reconstruction and Compression of Color Images Using Principal Component Analysis (PCA) Algorithm