About Me
👨‍💼Bonjour, je m'appelle Hicham Garrad, diplômé d’un Master en Business Intelligence & Big Data et passionné par la data science et l’intelligence artificielle. Installé en France et actuellement en Master 2 MIAGE – IA Appliquée à l’Université Côte d’Azur, je suis immédiatement disponible pour une alternance afin de mettre mes compétences en pratique dans un environnement stimulant.
Education
Master 2 MIAGE Intelligence artificielle appliquée
CurrentUniversité Côte d'Azur - Sciences, Biot
Master in Business Intelligence & Big Data Analytics
University Chouaib Doukkali
Honors Professional Degree Diploma BIG DATA
University Soultan Moulay Sliman
Honors DUT Computer Engineering
University Soultan Moulay Sliman
ProfessionalCompetences
Data Science
Core Skills
Create, Evaluate and Deploy Machine learning Models with Sklearn
Supervised learning: Regression, KNN, SVM, Logistic Regression and Decision Tree Models
Unsupervised Learning: K-Means for data Clustering
Processing Big Data and Distributed Systems with PySpark
Analysing Datasets with pandas, numpy and scipy
Data Visualization with matplotlib and seaborn
Databases and SQL for Data Science
Making data-driven decisions
Technologies & Tools
FeaturedProjects
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Breast Cancer Detection SaaS using Deep Learning:
Preprocessing and enhancement of medical images Design and training of models (classification, segmentation) Integration of models into a backend microservice for deployment via a REST API, with an active learning loop Development of an interactive web platform for assisted breast cancer diagnosis
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Fraud Detection
Ce travail est réalisé dans le cadre de mon projet de fin d'études effectué au sein de la société HPS, visant à développer un système de détection des fraudes monétique en utilisant des algorithmes de machine Learning.
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Big Data Pipeline for Churn Prediction with ML:
Creation of a pipeline using PySpark, Kafka, and Apache Airflow to process monthly and historical data stored in a Data Warehouse and MySQL. Integration of a churn detection model with Machine Learning and exposure of the results via FastAPI. Data visualization with Power BI.
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Revenue Forecasting for B2B SaaS Clients:
Development of a hybrid model combining ML and time series (XGBoost, Prophet) to predict monthly recurring revenue (MRR). Interactive visualization of forecasts and scenarios using Streamlit.
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Multi-Agent Recommendation System for an E-Commerce Platform:
Développement d'un système de recommandation personnalisé utilisant des agents intelligents et des modèles ML. Backend avec FastAPI, MongoDB, et frontend en Next.js.
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Plant Disease Detection System
Building a plant disease detection system, using AI to analyze images and identify diseases, facilitating rapid diagnosis of crop health issues through a microservice.
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Real-time Facial Emotion Recognition (CNN)
Building a real-time facial emotion recognition system using CNNs to analyze facial expressions instantly.
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City-Info
Conception et développement d'une application Android en Java qui facilite le processus de géolocalisation autour d'une ville se basent sur les différents points d'intérêts de citoyen.
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ORMVAO
Conception et développement d'une application Android en Java pour l'office régionale de mise en valeur agricole d'Ouarzazate (ORMVAO) qui facilite le processus de gestion des formations au sein de l'ORMVAO.
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Stock Management
Conception et développement d'une application de gestion des stocks qui permet de suivre toutes les informations des marchandises gérées dans un entrepôt.
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E-commerce Analysis
A comprehensive e-commerce data analysis project to extract insights from customer behavior, sales patterns, and market trends using advanced analytics techniques.
ProfessionalCertifications
Introduction to Data Engineering
List basic skills required for an entry-level data engineering role. Discuss various stages and concepts in the data engineering lifecycle. Describe data engineering technologies such as Relational Databases, NoSQL Data Stores, and Big Data Engines. Summarize concepts in data security, governance, and compliance.
Skills Gained
Introduction to Big Data with Spark and Hadoop
Apply Spark programming basics, including parallel programming basics for DataFrames, data sets, and Spark SQL. Use Spark's RDDs and data sets, optimize Spark SQL using Catalyst and Tungsten, and use Spark's development and runtime environment options.
Skills Gained
ETL and Data Pipelines with Shell, Airflow and Kafka
Describe and contrast Extract, Transform, Load (ETL) processes and Extract, Load, Transform (ELT) processes. Explain batch vs concurrent modes of execution. Implement ETL workflow through bash and Python functions. Describe data pipeline components, processes, tools, and technologies.