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mdhabibi
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mhabibi mdhabibi

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Senior AI and Data scientist | Ph.D. in Physics | IOP Trusted Reviewer

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mdhabibi/README.md

Hello World, I'm Mahdi, Data Scientist | AI and ML Engineer | Computational Physicist (Ph.D.)!

  • Senior data scientist and machine learning developer with more than 10 years of experience in data science.
  • Lead developer of recommendation system and AI applications at DealCircle GmbH.
  • Expert in XAI techniques to enhance decision-making transparency of black-box models.
  • Developer of the Open-Source Python Package (TelescopeML).
  • Lead developer of XAI module for TelescopeML (PyPI).
  • Developer of end-to-end AI/ML/DL projects, collaborating with cross-functional teams.

Connect with me:

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Professional Badges and Credentials

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Skills:


Projects

Projects Techniques Data Types Poster
SOLID Design Principles Tutorial
Learn SOLID design principles through interactive Jupyter notebook tutorials
Solid-principles, design-patterns, object-oriented-programming, software-architecture, code-quality
TelescopeML Open-Source Python Package
Deep Convolutional Neural Networks and Machine Learning Models for Analyzing Stellar and Exoplanetary Telescope Spectra
Deep CNN, Machine Learning, XAI, Bayesian optimization, Feature Engineering Timeseries, Tabular
Malaria Cell Classifier
Deep Convolutional Neural Networks and Machine Learning Models for Anomaly Detection in Microscopic Malaria cells.
Deep CNN, Data Augmentation, Feature Engineering, Image Processing, Optimization Image
BloodPy-Automated Blood Cell Classifier
Multi-Classification of Peripheral Blood Cells using Deep Convolutional Neural Networks and Machine Learning Models.
Deep CNN, Data Augmentation, Transfer Learning, U-Net, Image Processing, Statistical Analysis, OpenCV, Fine-tuning Image, Metadata
Dataset: Segmented Peripheral Blood Cells Using OpenCV
A Dataset of Segmented White Blood Cell Images Using Advanced Image Processing Techniques.
GrabCut, Morphological Operations, OpenCV Image, Binary Masks, Dataset
LIME for Macroscopic Medical Images
A Surrogate Model (Local Interpretable Model-agnostic Explanations) for Enhancing Transparency of Medical Diagnostics.
Deep CNN, LIME, XAI, Computer Vision, Optimization Image
CAM for Macroscopic Medical Images
Class Activation Mapping (CAM) Technique for Anomaly Localization Interpretability.
Deep CNN, CAM, XAI, Computer Vision, Data Analysis, Optimization Image
Automated Nucleus Detector
A Semantic Segmentation Solution for Automating Nucleus Detection of Microscopic Biomedical Images.
U-Net, Keras-tunner, Semantic Segmentation Image
FastAPI Questionnaire API
A FastAPI application to manage and retrieve questionnaire data with user authentication and custom error handling.
FastAPI, Authentication, Data Management, Error Handling, Shell API, CSV
Neural Compression
Advanced Autoencoder Architecture for Efficient Data Compression Losslessly.
Autoencoder, GenAI, SSIM, PSNR Image
LIME for ECG Classification
A Surrogate Model (Local Interpretable Model-agnostic Explanations) for Enhancing Transparency of TimeSeries.
CNN, LIME, Time Series Analysis Timeseries
Beta-Variational Autoencoders
Generative Learning (GenAI) with Beta-Variational Autoencoders.
Beta-VAEs, Latent Space Analysis Image
Movie Recommendation Systems
A comprehensive collection of movie recommendation systems, implementing collaborative filtering, content-based filtering, and Bayesian average techniques.
Collaborative Filtering, Content-Based Filtering, Bayesian Average Metadata, User Ratings, CSV
Falcon 9 rocket Predictor
A Data-Driven Project to Predict the Success of Falcon 9 Rocket Landings.
Data Wrangling, Feature Engineering, Web-Scraping, JSON Data Processing, SQL, Hadoop, Folium, Decision Tree Classification Unstructured Data, Tabular, Database
Variational AutoEncoders
A collection of Variational AutoEncoder (VAE) architectures developed by Keras deep learning framework.
Variational AutoEncoders, Exploratory Data Analysis (EDA) Image
Netflix Content Analysis
An Exploratory Analysis of Netflix's Vast Catalog to Uncover Trends and Insights into Content Distribution, Popularity, Quality, and Key Contributors.
Exploratory Data Analysis (EDA), Data Visualization, Statistical Analysis Metadata
Air Passenger Timeseries Analysis
An Exploratory Analysis of the Air Passengers Timeseries dataset, Uncovering Trends and Patterns in Air Travel Over the Years.
Timeseries Analysis, Log Transformation, Moving Averages, Seasonal Decomposition, Seasonal Adjustment Timeseries

Popular repositories Loading

  1. LIME-for-Time-Series LIME-for-Time-Series Public template

    LIME for TimeSeries enhances AI transparency by providing LIME-based interpretability tools for time series models. It offers insights into model predictions, fostering trust and understanding in c...

    Jupyter Notebook 15 3

  2. DeepLearning-VAE DeepLearning-VAE Public

    Exploring the depths of generative learning with a $\beta$-Variational Autoencoder ($\beta$-VAE) applied to the MNIST dataset for robust digit reconstruction and latent space analysis.

    Jupyter Notebook 7 1

  3. CNN-Predictor-for-Malaria_Cells-LIME-CAM CNN-Predictor-for-Malaria_Cells-LIME-CAM Public

    Enhanced CNN model for malaria cell classification, featuring Class Activation Mapping (CAM) as a non-agnstic technique for anomaly localization and LIME (Local Interpretable-agnostic Explanation) ...

    Jupyter Notebook 6 1

  4. Automated-Cell-Semantic-Segmentation-with-UNet Automated-Cell-Semantic-Segmentation-with-UNet Public

    A machine learning solution for automating nucleus detection in biomedical images, leveraging the U-Net architecture to accelerate medical research and disease treatment discovery.

    Jupyter Notebook 5 1

  5. Neural-Compression-with-Autoencoders Neural-Compression-with-Autoencoders Public

    Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests vario...

    Jupyter Notebook 2

  6. Capstone-Project-IBM Capstone-Project-IBM Public

    A data-driven project to predict the success of Falcon 9 rocket landings, crucial for cost analysis and competitive strategy in the space industry. Involves data manipulation in Pandas, JSON data p...

    Jupyter Notebook 1