Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
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Updated
Sep 2, 2020 - Python
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
👑 Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA
UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.
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…
✍️ An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.
Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
Explorative multivariate statistics in Python
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. 🌩️
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).
📈 🐍 Multidimensional synthetic data generation with Copula and fPCA models in Python
Maximum Covariance Analysis in Python
Implementation of Machine Learning Algorithms
Implementation of PCA/2D-PCA/2D(Square)-PCA in Python for recognizing Faces: 1. Single Person Image 2. Group Image 3. Recognize Face In Video
implement the machine learning algorithms by python for studying
A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
Machine learning is widely used in bioinformatics and particularly in breast cancer diagnosis. In this project, certain classification methods such as K-nearest neighbors (K-NN) and Support Vector Machine (SVM) which is a supervised learning method to detect breast cancer are used.
All codes, both created and optimized for best results from the SuperDataScience Course
Reconstruction and Compression of Color Images Using Principal Component Analysis (PCA) Algorithm
Feature selection for maximizing expected cumulative reward
Python module for Factorial Analysis : Simple and Multiple Correspondence Analysis, Principal Components Analysis
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