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A- TOPICS YOU CAN EXPLORE AND MASTER: 1- PYTHON FOUNDATIONS • Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming • Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code • Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management 2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS • Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design • Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use • Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies • Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges 3- DATA PREPARATION AND EXPLORATION • NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data • Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation • Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation 4- MATHEMATICAL FOUNDATIONS • Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics • Loss functions, gradients, distance measures, regularization, likelihood, and model complexity • Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms 5- SUPERVISED MACHINE LEARNING • Linear and polynomial regression, logistic regression, and regularized models • k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers • Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results 6- UNSUPERVISED LEARNING • Clustering using k-means, hierarchical clustering, and density-based methods • Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery • Method selection, evaluation of data structure, and interpretation of results without predefined labels 7- MODEL EVALUATION AND IMPROVEMENT • Training, validation, and test sets; cross-validation; hyperparameter optimization • Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2 • Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization 8- DEEP LEARNING • Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent • Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations • TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment 9- ARTIFICIAL INTELLIGENCE APPLICATIONS • Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models • Computer vision, image classification, fundamental principles of object detection, and image preprocessing • Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications 10- GENERATIVE AI AND LARGE LANGUAGE MODELS • Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation • Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant • Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation 11- TOOLS AND LIBRARIES • Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch • Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project • Additional libraries may be introduced depending on the selected specialization and dataset 12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION • Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results • University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications • Code review, debugging, documentation, reproducibility, model comparison, and communication of results ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- B- PERSONALIZED TUTORING: LEARNING HOW TO REASON Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected. My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly. Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan. The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach. A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps. You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level. My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.

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Homework help. Exam preparation. Preparing for job interviews. You can attend the course alone or with a group of 5 people maximum.

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I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples. Topics include: pyRevit fundamentals and setup Automating repetitive Revit tasks (model checks, plan generation, data extraction) Writing custom scripts for your firm's specific workflows Applying Python automation to real MEP/BIM projects

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Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning. This popularity means that Python is constantly evolving. It offers a wide range of tools and libraries, which are free and very varied. As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others. I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly. Beyond these general principles, there is no magic rule or method. Some approaches work with some students but not with others. Adaptation to individual needs is therefore the main objective of private lessons. So I will do my best to find what motivates and helps my student.

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Master Python with Personalized Courses Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels. Why Choose My Courses? Personalized Teaching Approach: Each course is tailored to your skill level and individual goals. Practical Experience: Learn by doing with real-world projects that build your understanding and skills. Ongoing Support: Get unlimited email support for any questions you have between sessions. As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey. Book Your First Lesson: Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.

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Don't settle for anything less than excellence. I am an Aerospace Engineer with a Master's degree in Quantum Physics and have completed Ph.D. work at the University of Cambridge in Computational Physics. Additionally, I have 4 years of experience developing MATLAB and possess deep programming skills in MATLAB/Simulink family, C/C++, Fortran, and Python. With over 12 years of tutoring experience, I have successfully guided more than 50 students worldwide to achieve distinction in various fields. Consistent results are my priority, and I strive for excellence in all aspects of my teaching. My lessons are customized to meet each student's unique needs and are designed to be engaging and insightful. Whether you are at a school level or require advanced or professional-level instruction, I offer support in the following areas: - Preparation for IB/IA, A-Levels, GCSE, University Entry, or equivalent. - Experience in preparing students to access world-class schools and universities, including Cambridge University, Oxford, Ivy League and other top institutions in the UK and US. - University levels (undergraduate and postgraduate). - High school studies and diploma programs. - Assistance with specific projects at a professional level, including job interview preparation. - Extensive experience working with children. Every lesson is meticulously planned in advance to ensure that it aligns with your goals and targets areas for improvement. I prioritize a dynamic and interactive learning experience, with one-on-one sessions tailored to your individual requirements. Lessons will be conducted via webcam, enabling you to connect from anywhere. I have a highly flexible schedule and can adapt to accommodate your needs. If you have any questions about my teaching method, availability, or pricing, please don't hesitate to reach out. I am here to assist you and provide the support you need.

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These courses are part of a structured and progressive training in Object-Oriented Programming (OOP) with JavaScript, designed for beginner or intermediate developers who want to understand in depth how the language works, write clearer, more maintainable code and prepare themselves calmly for modern frameworks like React ⚛️. Object-Oriented Programming is often perceived as complex or abstract. My goal is simple: to make it logical, concrete, and immediately applicable. 🎯 Training Objectives Upon completion of this training, you will be able to: Understanding what Object-Oriented Programming really is (and when to use it) Create and manipulate objects in JavaScript in a clean and efficient way Use ES6 classes, constructors, and methods with confidence Mastering this, the prototype, and the instantiation logic Apply encapsulation, inheritance, and polymorphism without confusion Avoiding common mistakes made by OOP beginners Structure your JavaScript code like a professional developer 📖 Training Plan – Object-Oriented Programming in JavaScript 1. Introduction to Object-Oriented Programming 🧠 Understanding the concept, objectives and benefits of OOP. 2. Procedural Programming vs. OOP Why unstructured code quickly becomes unmanageable. 3. Objects in JavaScript Properties, methods and representation of the real world. 4. The keyword this Understanding the execution context (often poorly understood). 5. Limitations of simple objects Why duplicating code is a bad idea. 6. Constructive functions Create multiple objects from the same model. 7. The keyword new What it's actually doing under the hood. 8. The prototype Sharing methods and memory optimization. 9. ES6 Classes Modern syntax and best practices. 10. The builder Proper initialization of objects. 11. Data Encapsulation Protect the internal state of objects. 12. Inheritance between classes Reusing code intelligently. 13. The keyword super Communication between parent and child in the classroom. 14. Polymorphism The same behavior, several forms. 15. Composition vs. Inheritance Choosing the right architecture. 16. Best practices in OOP Write readable, scalable, and maintainable code. 17. Common mistakes made by beginners Pitfalls to absolutely avoid. 18. Guided practical exercise Creation of a concrete class (product, user, etc.). 19. Assessment Quiz (Multiple Choice Questions) To validate the actual understanding of the concepts. 🛠️ Teaching method: Understand before writing This training program is based on a progressive and pragmatic approach: Clear and illustrated explanations Concrete examples from real projects Simple but effective exercises Constant questioning to avoid rote learning Adaptation to the learner's level and pace Here, we don't "recite OOP" — we understand it. 🚀 Learner's result At the end of the training, you will not only know how to write a JavaScript class. You will know: 1- Why does it exist? 2- When to use it 3- and when not to use it You will leave with: a solid understanding of OOP a cleaner and more professional code an ideal foundation for learning React, Node.js or any other modern framework