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An entry-level deep learning engineer who takes pride in building models that translate data points into business insights and help overcome business and social challenges. I am eager to apply the same knowledge to real-world social, medical and business problems.

- Neural Networks => Project: Predicting Bike-Sharing Patterns,
- Convolutional Neural Networks => Project: Dog Breed Classifier,
- Recurrent Neural Networks => Project: Generate TV Scripts,
- Generative Adversarial Networks => Project: Generate Faces,
- Model Deployment => Project: Model Deployment (AWS SageMaker)


Udacity

- Ranked 1st place in my class by developing an event management mobile multi-platform app
- Studied Full stack, Mobile and Multiplatform Development, Network Engineering, Big Data & Data Science concepts and scored high in each 4-month project in these subjects

National School of Applied Sciences

- Introduction to Data => Project: I interpreted Malaria data using Data Visualizations,
- Descriptive Statistics => Project: I analyzed Udacity Survey Data,
- SQL for Data Analysis => Project: I analyzed the Chinook Database,
- Data Visualization => Project: I built Data Dashboards using Tableau on the 2015 Flight Delays and Cancellations data from Kaggle


Remote - Machine Learning Engineer/Task Manager

Sara EL-ATEIF

Omdena

Managed the treatment scope task to design a doable solution and trained the BERT model to classify Posttraumatic Stress Disorder patients into affected or not.

Built and optimized an API that extracts, recognizes, checks and reports stored typing errors from Moroccan CINE. I reached an extraction and recognition rate of 90% to 100% from images with average quality and defined a standard to improve the rest.
- Image processing and edge detection: OpenCV, scikits-image, HED algorithm 
- OCR: pytesseract
- App deployment: Flask framework


BEREXIA Morocco, Casablanca

Data Scientist/Computer Vision Engineer

Trained the SSD model for real-time road damage detection and classification in participation to the IEEE Big Data 2018 Cup Challenge, using Keras and TensorFlow 1.0

LTI ENSAJ Laboratory

Deep Learning Research Intern

Developed RFC security reinforcement measures adapted to the scenarios specific to Lydec's information system architecture.

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