Mohcine Madkour's repositories
diabet2risk
Type-2-Diabetes-Risk-Prediction
renewcastapp
A web app that provides forecasts for renewable energy generation of EU countries, based on Streamlit and sktime.
covid-cxr-interpret
Deep Neural network model for classifying and interpreting chest X-rays by presence of COVID-19 features
Dropout-for-Deep-Learning-Regularization-explained-with-Examples
https://medium.com/@mohcine.madkour/dropout-for-deep-learning-regularization-explained-with-examples-dee81f0de35a
mohcineblog
Personal Blog
mohcinemadkour.github.io
Mohcine's Blog
AKI-risk-Calculator-
Real time AKI risk Calculator using changes in serum creatinine
BloggedProjects
Source Code for Project with blogs
Covid_CXR_Classifictaion
How I achieved ~ 97% Accuracy in Covid-19 Diagnosis from Chest X-rays Images
devops-jenkins-sonarqube
DevOps - Jenkins + SonarQube Integration
explainaibility-of-model-based-feature-importance-
Variable importance is central to scientific studies, including the social sciences and causal inference, healthcare, and other domains. However, explainability of variable importance is lacking. This is problematic: what if there were multiple well-performing predictive models, and a specific variable is important to some of them and not to others? In that case, we may not be able to tell from a single well-performing model whether a variable is always important in predicting the outcome. In order to circumvent that issue feature importance obtained from the model being trained can be explained using bayesian linear model
Interpretability-Vs-Explainaibility
In the new era of Intelligent Systems, interpretable machine learning model becomes important, but there is still misconception of interpret-able and explainable machine learning model, what is the difference and which path is the most beneficial to take?
Interpretable-Causal-Inference
In this repo I create methods in causal inference and Bayesian nonparametrics that: 1. can be used in practice, featuring scalable implementations that facilitate their application to real data; 2. are designed to handle the complexity inherent in real data without making naive assumptions; and 3. have exceptional predictive accuracy, even as they boast other desirable features like interpretability and uncertainty quantification.
MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
Nvidia-DLI-s-C-FX-01
Nvidia's DLI course for Fundamentals of Deep Learning for Computer Vision
Predictive-and-visual-analysis-of-Primary-Care-patient-information-
Predictive and visual analysis of Primary Care patient information