Freelancer profile translated to English.
Back to original languageAbout Axel
Axel Guérin – Data Scientist & Machine Learning Engineer
👋 Hello, I am Axel, an expert in data science and machine learning, currently completing my PhD in data science at the University of Angers.
My Background:
🎓 PhD in Data Science – University of Angers
During my PhD, I developed unsupervised learning algorithms, optimized hyperparameters, and designed data pipelines for industrial applications. My work has also been presented at international conferences.
💼 Professional Experience
- Development of predictive algorithms: Expertise in machine learning models, from design to production.
- Optimization and visualization of results: Creation of interactive tools for informed decision-making.
- Teaching & Mentoring: Training in data science and support for technical teams.
Key Skills:
✔ Languages & Tools: Python, R, SQL, Java, C/C++, JavaScript...
✔ Advanced Modeling: Supervised, unsupervised learning, algorithm optimization
✔ Collaboration: Multidisciplinary teamwork, complex project management
Why Work With Me?
I am convinced that data is at the heart of innovation. My goal is to help my clients transform their data into real value through tailor-made solutions. With a results-oriented approach, I am ready to take on your data science challenges and contribute to the success of your projects.
What I Offer on Malt:
- Design and deployment of machine learning models
- Advanced data analysis and visualization
- Consulting and training in data science and machine learning
📍 Based in France, available for remote projects.
💡 Need an AI expert for your project? Do not hesitate to contact me to discuss your needs!
French
Native or bilingual
Remote only
Primarily works remotely
Experience
- Université d'AngersPhD Student CIFRE Data ScienceApril 2021 - Today (5 years and 2 months)Angers, France
Extraction and management of customer knowledge through unsupervised learning methods
My thesis explores Self-Organizing Maps (SOMs), a powerful unsupervised learning tool, and proposes innovative contributions to improve their performance and applicability in various contexts. By focusing on hyperparameter tuning and evaluation, I have developed multi-criteria approaches that better capture the quality of SOM projections.A significant part of this work is dedicated to optimizing SOMs through advanced techniques such as Bayesian optimization and exploring key parameters, including map size, learning rate, and number of iterations. My experiments include applications on bi- and three-dimensional maps, demonstrating how precise hyperparameter tuning can improve both the accuracy and robustness of the results.Furthermore, I have proposed combined metrics and scores to evaluate SOMs more holistically, considering the trade-offs between result accuracy and computational constraints. These contributions not only optimize SOMs for complex datasets but also broaden their scope of applications in various fields, such as data visualization, segmentation, and dimensionality reduction.The results of my thesis have been published in scientific papers and presented at international conferences, highlighting the impact and recognition of my work within the scientific community. These contributions lie at the intersection of algorithmic research and practical applications, offering valuable tools and insights for making the most of SOMs in unsupervised data processing and analysis. - Université catholique de l'OuestUniversity TeachingSeptember 2020 - June 2024 (3 years and 10 months)Angers, FranceDuring my years at UCO, I had the opportunity to teach a range of courses in computer science, programming, and data science to undergraduate and graduate students. This experience allowed me to develop solid pedagogical skills and guide students in acquiring technical and practical knowledge.Courses and Workshops Taught:Programming and Computer Science
- Teaching fundamental programming languages such as Python and Java.
- Introduction to object-oriented programming principles and data structures.
- Implementation of simple to complex applications to illustrate theoretical concepts.
Introduction to Data Science- Training in basic data science concepts, including data collection, cleaning, and analysis.
- Use of Python and specialized libraries (pandas, NumPy, Matplotlib) for exploratory data analysis and visualization.
Machine Learning- Introduction to supervised and unsupervised learning models, including regressions, decision trees, and k-means.
- Use of scikit-learn for modeling, training, and evaluating predictive models.
Practical Projects and Case Studies- Supervision of projects allowing students to apply their skills to concrete problems.
- Personalized advice on project management, from design to deployment.
Skills Developed:- Pedagogical Approach: Simplification of complex concepts in computer science and data science, adapted to different student levels.
- Versatility: Teaching various courses, covering both programming and data science skills.
- Adaptability: Ability to adjust courses and exercises based on specific student needs and technological advancements.
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