Fill out the form below to get in touch, or send an email directly to hamoutnianas@gmail.com
Kudos AI is built on the belief that sharing knowledge accelerates learning for everyone. Whether you are a fellow researcher, a student working on a class project, or a professional looking to apply machine learning in your organization, I welcome hearing from you. Collaboration leads to better ideas, and many of the tutorials and projects published on this site have been shaped by feedback and suggestions from readers just like you. Your input helps keep the content relevant, accurate, and genuinely useful to the community.
Project Collaboration — If you have an idea for a machine learning project and would like to work together, I am always open to discussing new topics. Past collaborations have covered areas such as computer vision, natural language processing, and time-series forecasting, and I am happy to explore reinforcement learning, generative models, or any other field where data meets decision-making.
Questions and Feedback — If you spot an error in a tutorial, have a question about a specific code snippet, or want to suggest an improvement, please let me know. Constructive feedback helps me keep the content accurate and up to date. Every correction and suggestion is appreciated and credited where appropriate.
Consulting and Professional Inquiries — With a background in actuarial science, statistical modeling, and applied machine learning, I am available for consulting engagements that require rigorous quantitative analysis. Areas of expertise include predictive modeling, risk assessment, data pipeline design, model evaluation, and deployment strategies for production environments. I have experience working with insurance, finance, and technology companies across Africa and the MENA region.
Speaking and Writing — If you organize meetups, webinars, or conferences related to data science and artificial intelligence, I am open to speaking opportunities. I am also available for guest articles and technical reviews in publications that cover machine learning, deep learning, and applied statistics. Topics I enjoy presenting include practical model deployment, the mathematics behind popular algorithms, and lessons learned from real-world data projects.
I typically aim to respond to every message within two business days. If your inquiry is time-sensitive, please mention that in the subject line so I can prioritize it. For general questions about tutorials or code, you may also find answers in the comments and documentation within each project's GitHub repository. You can also connect with me on LinkedIn or X (Twitter) for quicker exchanges and updates on new content.