Continuation of my machine learning works based on Subjects....starting with Evaluating Classification Models Performance

Machine Learning- Unsupervised Learning(PCA)

Application of principal component analysis capturing non-linearity in the data using kernel approach

Video Face Recognition System with Java and Eigen-Faces (Principal Component Analysis). Undergraduate Thesis - Computer Science.

Analysing different dimensionality reduction techniques and svm

Implimenting PCA using numpy and comparing the results

NUS Pattern Recognition module graded assignments

Autoencoder model implementation in Keras, trained on MNIST dataset / latent space investigation.

L'analyse des composantes principales essaie de trouver les axes principaux qui sont des variables décorrélées qui décrivent au mieux nos données.

Use unsupervised machine learning techniques to analyze cryptocurrency data

In this project, we use differents methods to transform our dataset (usually dimension modification) before making prediction thanks to machine learning and regressions.

Principal Component Regression - Clearly Explained and Implemented

MITx - MicroMasters Program on Statistics and Data Science - Machine Learning with Python - Second Project

Cluster population demographics to find a companies target customer base

Uses K-Means unsupervised machine learning algorithm and Principal Component Analysis to cluster cryptocurrencies based on performance in selected periods.

Principle Component Analysis

Tutorial- data Pre-processing

PCA For Dimension Reduction And Visualization, Temperature-Yield Prediction Via Linear Regression, And Linear Fit Optimization Using Gradient Descent.

The database was created with records of absenteeism at work from July 2007 to July 2010 at a courier company in Brazil. The objective here is to predict for each new individual, whether he is going to be absent for more than 3 hours or no (3 hours is the median for the absenteeism hours).