Freelancer profile translated to English.
Back to original languageAbout Jamal
Data Scientist, for 4 years passionate about Machine Learning and Deep Learning. I develop in Python and use various libraries (Tensorflow with Keras, Pandas, Plotly for visualization, ...). I enjoy working on complex problems and deploying innovative solutions.
I have worked on numerous data-related topics and have professional experience in:
• Image Processing: Identity document processing project.
- Very diverse documents (orientation, quality, format).
- Document standardization and classification.
- Extraction of textual data contained in images and documents.
• Image Processing: Product sheet photo provider project.
- Automation of document and image processing.
- Standardization of document structures.
Tech: Python, Libraries used: Pillow, Tesseract, OpenCV.
• Email Processing:
- Retrieval of raw email data and information.
- Processing of raw information to extract intentions and entities from emails (using NLP).
- Detected information is classified into a usable format for business use (RPA flow) and decision-making.
Tech: NLP (segmentation, tokenization, lemmatization), validation by distance and graph theory.
• Cryptocurrency Analysis:
- Time series analysis using machine learning and deep learning.
- Development of predictive models, creation of a trading aid tool using machine learning techniques applied to financial data.
Tech: Tensorflow, Keras, Sklearn, Seaborn, Matplotlib, and LSTM.
French
Native or bilingual
Can work on-site
Paris (up to 50km)
Experience
- H4H Humans4HelpData scientistMarch 2021 - June 2021 (3 months)Paris, FranceImage processing;
- Identity document processing project:
- Very diverse documents (orientation, quality, format).- Document standardization and classification.- Extraction of textual data contained in images and documents.- Product sheet photo provider project:
- Automation of document and image processing.- Standardization of document structures. Tools used: Python, Libraries used: Pillow, Tesseract, OpenCV. Email processing:- Retrieval of raw email data and information.- Processing of raw information to extract intentions and entities from emails (using NLP).- Detected information is classified into a usable format for business use (RPA flow) and decision-making. Tools used: NLP (segmentation, tokenization, lemmatization), validation by distance, graph theory. Cryptocurrency data analysis:- Time series analysis using Machine learning and Deep learning.- Development of predictive models, creation of a trading aid tool using Machine Learning techniques applied to financial data. Tools used: Tensorflow, Keras, Sklearn, Seaborn, Matplotlib. - LIRMMResearch internship on the topic "Representation of a Node in the Cubic Network"February 2019 - June 2019 (4 months)Montpellier, FranceIntroduce an algorithm that integrates planar 4-regular graphs of Hamiltonian nodes starting with the Hamiltonian cycle. Then, unbounded edges and bounded edges in a cubic network.
- LIRMMStudent project in NLP on sentiment extractionNovember 2018 - December 2018 (1 month)Montpellier, FranceText classification using Deep Learning relies on powerful models such as LSTM, Seq2Seq, Transformers, and BERT, capable of extracting rich contextual representations to improve automatic natural language processing.
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Education
- Master 2, MONEY, BANKING, FINANCE, INSURANCE Actuarial TrackUniversity of Montpellier2021Master 2 , MONNAIE, BANQUE, FINANCE, ASSURANCE Parcours Actuariat
- BIG DATA, DATASCIENCE AND RISK ANALYSIS UNDER PYTHON, big data, data science, risk analysis: neural networks and machine learning.FDS2021BIG DATA, DATASCIENCE ET ANALYSE DES RISQUES SOUS PYTHON, le big data, la data science, l'analyse des risques: réseaux de neurones et les machines learning.