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Course 4: Unsupervised Machine Learning
A course on extracting hidden patterns, structures, and groupings from unlabeled data.
What's this Course About?
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Simplifying Complex Data
Learn how to use Principal Component Analysis (PCA) to reduce multiple variables into simple maps, uncover hidden patterns, and guide better business decisions. -
Clustering vs. Filtering
Understand the difference between scientific market clustering and basic filtering, and gain tools to define and profile market niches using data analysis techniques. -
Practical, Consultant-Level Skills
Apply methods across two technologies with hands-on practice, enabling you to track pattern evolution and act as a consultant rather than just an expert.
Eng. Walid Semaan
Professor Walid Semaan is an MIT-certified data scientist and AI strategist with four certificates in Data Science, Machine Learning, AI for Business Strategy, and NLP. He is the founder and president of Matrix TRC’s Data Science and AI Academy, in partnership with the Lebanese American University, where he co-leads the executive diploma in AI and Deep Learning.
Holding degrees in engineering (ESIB), finance and marketing (ESCP), and an MBA from Paris-Dauphine–Sorbonne, Walid created the award-winning Triple One Analytics platform, recognized as Best Innovative ICT Project at the Arab Golden Chip Awards. With 100+ international consulting missions and thousands of teaching hours, he brings deep expertise in machine learning, generative AI, forecasting, and data visualization.
He has trained and advised top institutions worldwide, including the Central Bank of Lebanon, PwC, Smart Dubai, Sanofi–Merck, and the Ministry of Health (KSA). His mission is to equip professionals and organizations with advanced AI tools to drive innovation and impact.
