A specialized teacher with extensive experience in teaching computer science college curricula. And different programming languages: Python, C++, C, Java, Html, Css, JavaScript. He has specialized courses in programming from the University of Michigan, USA. I have my own scientific method to gradually train the student to become a professional in any programming language. You must have your computer ready before the lesson.

💻 Introduction to Python and Algorithmic Programming Full title: Python programming and introduction to algorithms for beginners and high school students (high school & university level) (100% distance learning — for high school students, college students, beginners or adults retraining) Programming is an essential skill today, whether for studies, personal projects, or the professional world. But when you're just starting out, it's easy to feel lost when faced with seemingly complex lines of code or computer logic. That's where I come in! With a progressive, caring, and structured approach, I help students get to grips with Python—one of the most accessible and powerful languages—while discovering the basics of algorithms in a simple and concrete way. My goal is to transform the apprehension of coding into curiosity and joy of learning, and to lay solid foundations so that each student progresses with confidence. 🎯 Course objectives Learn the fundamentals of Python programming from scratch. Develop logical and algorithmic reflexes. Understand and build simple but useful programs. Inspire a taste for programming through accessible and concrete projects. 📚 Course content ✔ Python Basics – Syntax, indentation, comments – Variables, data types (numbers, strings, lists...) – Conditions (if, elif, else) – Loops (for, while) – Simple functions – Notions of modules and libraries ✔ Algorithms for beginners – Understand what an algorithm is – Writing in pseudo-code and Python – Sorting algorithms (bubble sort, insertion sort, etc.) – Searching for items in a list – Getting Started with Recursion – Simple optimization and complexity ✔ First practical projects – Creation of mini-games (e.g.: guess the number, rock-paper-scissors) – Simple calculator or unit converter – Automation of basic tasks (for example: automatic sorting of a list of students) – Small personalized projects based on the student’s interests 🧭 How the sessions work 1️⃣ Assessment of level and objectives (complete initiation, reinforcement, preparation for a competition or a project). 2️⃣ Tailor-made progression plan, with concepts covered step by step. 3️⃣ Alternating theory/practice to quickly develop autonomy. 4️⃣ Exercises, mini-challenges and projects to apply each concept learned. 5️⃣ Corrections and detailed explanations for each difficulty encountered. 6️⃣ Personalized monitoring, with regular assessments and continuous adaptation of the pace. 🌍 100% online courses – modern and adapted teaching methods Sessions via Zoom, Google Meet, or any other tool of your choice Interactive materials provided after each session: annotated codes, PDF files, exercises, tutorials Flexible hours, compatible with busy schedules and time differences (ideal for students living in the Gulf countries or elsewhere) Possibility of individual or small group lessons (siblings, classmates, etc.) 👨‍🎓 For whom? High school students or students in a technology/science stream wishing to prepare for higher education Students at the beginning of a computer science or science course Adults in professional retraining, curious to learn to code Complete beginners wishing to develop their digital skills With me, you'll learn to think like a programmer, not just copy code. You'll gain logic, rigor, and autonomy—valuable assets for the future. If you have any questions or would like to discuss the most suitable path, please do not hesitate to contact me. I am here to guide you with enthusiasm and kindness.

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.