Artificial Intelligence and programming become much easier when you understand the reasoning behind the algorithms—not simply memorize Python syntax or use AI tools as a black box. I am a PhD-qualified engineer, university professor, researcher, programmer, and multidisciplinary tutor with more than 30 years of experience across teaching, technical training, engineering, information systems, quantitative analysis, research, data mining, programming, and intelligent knowledge-based systems. This class provides a structured and personalized pathway for beginners, school and university students, researchers, engineers, professionals, career changers, and adult learners. Depending on your goals, we can focus on Python programming, computational problem solving, automation, machine learning, artificial intelligence, or a coherent progression connecting them. PYTHON PROGRAMMING & COMPUTATIONAL THINKING • Python installation and development environments • Variables, data types, operators, and expressions • Input, output, and program flow • Conditional statements and decision making • For loops and while loops • Functions, parameters, return values, and scope • Strings and text processing • Lists, tuples, sets, and dictionaries • File handling and data input/output • Error handling and exceptions • Modules, packages, and reusable code • Object-oriented programming • Algorithms and computational problem solving • Debugging and systematic error correction • Code organization, readability, and good programming practices SCIENTIFIC COMPUTING, DATA & AUTOMATION • NumPy for numerical computing • pandas for structured data manipulation • matplotlib for visualization • Scientific and engineering calculations • Automation of repetitive tasks • Data processing workflows • Working with files and external data • Introduction to APIs when relevant • Python and SQL workflows • Research and quantitative applications • Project development from idea to working solution MACHINE LEARNING • Foundations of machine learning • Supervised and unsupervised learning • Regression and classification • Clustering and pattern discovery • Decision trees and rule-based approaches • Feature selection and data preparation • Training, validation, and testing • Model evaluation and performance metrics • Overfitting and underfitting • Bias, variance, and generalization • Model comparison and interpretation • Predictive modelling and data mining • Neural-network foundations ARTIFICIAL INTELLIGENCE & INTELLIGENT SYSTEMS • Foundations and major branches of Artificial Intelligence • How intelligent systems represent, classify, predict, and support decisions • Knowledge representation concepts • Ontologies and structured knowledge • Rule-based reasoning and expert-system foundations • Intelligent decision-support systems • Generative AI and large language model concepts • Prompt design and effective AI-assisted workflows • AI limitations and hallucinations • Bias, privacy, ethics, and responsible AI • Applications in engineering, research, business, education, and professional decision making Depending on your goals, practical work may involve Python, NumPy, pandas, matplotlib, relevant machine-learning libraries, Weka, SPSS Modeler, structured datasets, or modern generative-AI tools. My teaching approach follows a clear progression: understand the problem → design the logic → represent and prepare the data → write or select the method → test it → evaluate the output → debug or improve it → interpret the result → apply it responsibly I do not simply provide finished code, demonstrate isolated commands, or recommend an AI model because it is popular. I help you understand why a method works, what assumptions it makes, when it should be used, how to evaluate its results, where it may fail, and how to improve the solution. We can work with your course syllabus, programming exercises, existing code, error messages, dataset, research problem, AI project, automation task, engineering application, model output, or professional use case. Whether you are writing your first Python program, preparing for a university course, debugging a project, automating a professional task, learning machine learning, or exploring advanced AI applications, I will adapt the sessions to your level, objectives, and pace. My goal is to help you become an independent computational problem solver who can understand, build, evaluate, and apply intelligent solutions—not merely copy code or use AI tools without understanding them.

My courses are aimed at students of all levels and objectives: refresher courses, preparation for tests, exams or in-depth study of concepts. Each session is tailored to the student's needs. We begin by identifying difficulties, then review the concepts covered in the lesson before applying them through exercises of increasing difficulty. I place great importance on understanding concepts and developing reasoning skills rather than rote learning. The courses take place exclusively online, which allows for efficient work on the course material, exercises and corrections in real time. As an engineering student at ENSIMAG and a former student of MPSI-MP preparatory classes, I leverage my strong background in mathematics and computer science to guide students with rigor, patience, and effective teaching methods. My goal is to help them make lasting progress, gain confidence, and become independent in their work.