mohcinemadkour
An inquisitive and thorough researcher by training, a creative problem-solver, and a data scientist at heart with advanced technical and quantitative skills.
Pinned Repositories
BerkeleyX-CS100.1x-Big-Data-with-Apache-Spark
This repository contains code files specifically IPython notebooks for the assignments in the course "Introduction to Big Data with Apache Spark" by UC Berkeley and Databricks on edX
bioblitz
Automatically exported from code.google.com/p/bioblitz
covid-cxr-interpret
Deep Neural network model for classifying and interpreting chest X-rays by presence of COVID-19 features
Detection_Of_Melanoma_Cancer_Using_Deep_Learning
Melanoma Cancer Detection Using Deep Learning
diabet2risk
Type-2-Diabetes-Risk-Prediction
Dropout-for-Deep-Learning-Regularization-explained-with-Examples
https://medium.com/@mohcine.madkour/dropout-for-deep-learning-regularization-explained-with-examples-dee81f0de35a
hcaf
Healthcare-AnalytX-Framework (HCAF) is an interdisciplinary engagement model that involves a Healthcare Data Hub with MySQL and Mirth Connect and focus on HL7 version 2 messages (In Progress)
ML-Workshops
Some Spare Projetcs : Data Visualization, Data analysis
regimedet
Detection of Active Power Consumption in Energy Data Using Matrix Profile
renewcastapp
A web app that provides forecasts for renewable energy generation of EU countries, based on Streamlit and sktime.
mohcinemadkour's Repositories
mohcinemadkour/diabet2risk
Type-2-Diabetes-Risk-Prediction
mohcinemadkour/renewcastapp
A web app that provides forecasts for renewable energy generation of EU countries, based on Streamlit and sktime.
mohcinemadkour/covid-cxr-interpret
Deep Neural network model for classifying and interpreting chest X-rays by presence of COVID-19 features
mohcinemadkour/Dropout-for-Deep-Learning-Regularization-explained-with-Examples
https://medium.com/@mohcine.madkour/dropout-for-deep-learning-regularization-explained-with-examples-dee81f0de35a
mohcinemadkour/hcaf
Healthcare-AnalytX-Framework (HCAF) is an interdisciplinary engagement model that involves a Healthcare Data Hub with MySQL and Mirth Connect and focus on HL7 version 2 messages (In Progress)
mohcinemadkour/mohcineblog
Personal Blog
mohcinemadkour/mohcinemadkour.github.io
Mohcine's Blog
mohcinemadkour/regimedet
Detection of Active Power Consumption in Energy Data Using Matrix Profile
mohcinemadkour/AKI-risk-Calculator-
Real time AKI risk Calculator using changes in serum creatinine
mohcinemadkour/BloggedProjects
Source Code for Project with blogs
mohcinemadkour/Clustering-Smart-Meter-Data-tutorial
mohcinemadkour/COVID-19
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
mohcinemadkour/Covid-cxr-config
mohcinemadkour/Covid_CXR_Classifictaion
How I achieved ~ 97% Accuracy in Covid-19 Diagnosis from Chest X-rays Images
mohcinemadkour/credit-eda-case-study-v-1
mohcinemadkour/DeepADoTS
Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
mohcinemadkour/devops-jenkins-sonarqube
DevOps - Jenkins + SonarQube Integration
mohcinemadkour/examples
TensorFlow examples
mohcinemadkour/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
mohcinemadkour/Goemtrack-Documentation
mohcinemadkour/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?
mohcinemadkour/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.
mohcinemadkour/MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
mohcinemadkour/MLModel-Interpretation-
mohcinemadkour/Nvidia-DLI-s-C-FX-01
Nvidia's DLI course for Fundamentals of Deep Learning for Computer Vision
mohcinemadkour/observations
mohcinemadkour/opendatasets
mohcinemadkour/Predictive-and-visual-analysis-of-Primary-Care-patient-information-
Predictive and visual analysis of Primary Care patient information
mohcinemadkour/xpressmath
mohcinemadkour/xpressmath-source