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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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15Students

Are you a university student, engineer, or professional who needs to actually use data — not just learn theory about it? This course is built around real problems and real code. We skip the textbook formulas and go straight to applying statistics and data science the way professionals do: with Python (pandas, NumPy, scikit-learn, matplotlib) and R (RStudio). What we cover, adapted to your level and goals: - Descriptive and inferential statistics (the ones that actually matter) - Data cleaning, exploration, and visualization - Regression, classification, and intro to machine learning - Time series and forecasting basics - R for statistical analysis and academic research Who this is for: - University students in statistics, economics, engineering, or biology - Professionals wanting to move into data analysis or data science - Researchers who need to process and present data properly I use Python and R professionally as a working engineer — everything I teach comes from real application, not just academic exercises.

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5Students

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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Learn to code with method and logic Whether it's to succeed in the NSI (Digital Sciences and Technology) specialization in high school, design personal projects, or prepare for higher scientific studies, mastering code relies on solid algorithmic thinking. I help students understand the structure of programming languages and the logic of data. Subject areas and languages taught: Algorithms & Logic: Designing data structures and solving problems. Programming Languages: Python, C/C++, C# and Java. Data Management: Analysis and SQL queries / databases. Basic Web Development: HTML & CSS for creating structured pages. The goal is to take the student from simply writing code to true autonomy in development.

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4Students

Are you looking to deepen your knowledge of physics or master advanced techniques in Python programming? This course is designed to help you achieve a level of academic and technical excellence, whether you are a high school student, university student or professional looking to develop your skills. With an interactive and effective teaching approach, you will benefit from personalized support to overcome obstacles, grasp complex concepts and improve your performance. Whether it is to succeed in difficult exams, high-level competitions or to create powerful applications in Python, this course will provide you with all the tools necessary to achieve your goals. 🎯 Why Choose This Course? Advanced and Expertise Level: In-depth content and effective methodology to master complex topics in physics and programming. Personalized Courses: Sessions adapted to your needs, your pace and your level. Interactive Online Learning: Dynamic online courses with screen sharing and audio interaction for enjoyable and engaging learning. Concrete Projects and Practical Applications: Development of practical projects to implement theoretical concepts. Intensive Preparation for Exams and Competitions: Rigorous training with complex exercises and exam simulations to guarantee your success. Flexibility and Comfort: Learn from home, according to your schedule, without having to travel. 🔬 Advanced Physics – From High School to University This module offers a complete and in-depth program to prepare you for demanding secondary school exams, preparatory classes and scientific university courses: 1. Secondary Physics (High School and Preparatory Classes) ⚙️ Classical Mechanics: Kinematics, dynamics, Newton's laws, energy and work, oscillations. 🌊 Waves and Vibrations: Wave propagation, interference, diffraction, acoustics. ⚡ Electricity and Magnetism: Electric circuits, electrostatics, magnetostatics, electromagnetic induction. 🔦 Optics: Geometric optics (lenses, mirrors), wave optics (interference, diffraction). 🌡️ Thermodynamics: Laws of thermodynamics, thermodynamic cycles, entropy, changes of state. 2. University and Advanced Physics 🔄 Analytical Mechanics: Lagrangian, Hamiltonian, and generalized coordinates. 🌐 Advanced Electromagnetism: Maxwell's equations and the propagation of electromagnetic waves. 🧪 Quantum Physics: The postulates of quantum mechanics, the wave function, and the Schrödinger equation. ☢️ Nuclear and Particle Physics: The structure of the nucleus, radioactivity, and fundamental interactions. 🌌 Special Relativity: Lorentz transformations, time dilation, and length contraction. 3. Intensive Preparation for Exams and Competitions 📘 Scientific methodology: Learn to analyze statements, structure responses and write clearly and precisely. 📝 Application exercises and past papers: Intensive training to master the concepts and pass your exams. 🔎 Solve complex problems with detailed explanations and effective strategies. ⏰ Time management: Practical tips to improve your time management during exams. 🐍 Advanced Python Programming – Focused on Efficiency This module will teach you how to program efficiently and effectively in Python, with an emphasis on best practices and advanced techniques: 1. Mastering Advanced Concepts in Python 🔠 Advanced Syntax and Best Practices: Deepening of Python concepts. 📦 Object-Oriented Programming (OOP): Abstract classes, interfaces, and design patterns in Python. 🔄 Functional Programming: Using lambda, map, filter, reduce, generators and iterators. ⚡ Asynchronous Programming: Implementing asyncio for fast and responsive applications. 🧪 Unit Testing and Code Quality: Use of pytest, code coverage, and CI/CD. 2. Performance Optimization 🚀 Algorithm Optimization: Analysis of algorithmic complexity and use of efficient data structures. ⚙️ Profiling and Debugging: Performance evaluation with cProfile and code improvement. 🔒 Security and Robustness: Writing secure code and handling exceptions appropriately. 3. Practical Projects and Advanced Applications 🌐 Web Applications: Building high-performance web applications using Flask and FastAPI. 📊 Data Science and Machine Learning: Exploitation of Pandas, NumPy, Scikit-learn and TensorFlow. 🕸️ Advanced Web Scraping: Complex data extraction using BeautifulSoup and Selenium. 🤖 Automation and Efficient Scripts: Automation of tasks and development of efficient scripts. 🧑‍🏫 Methodology and Pedagogical Approach: Learning by doing: Each theoretical concept is directly implemented through practical exercises. Interactive Online Teaching: Using audio and screen sharing for seamless communication and dynamic learning. Personalized monitoring: Regular support to assess your progress and answer all your questions. Concrete projects: Development of complete projects to apply your programming skills. Motivation and Confidence: A positive and encouraging approach to build your confidence in your abilities. 🎓 For whom? This course is intended for: - High school and preparatory class students who aspire to academic excellence. - University students in science and computer science wishing to deepen their knowledge. - Candidates for scientific competitions who are preparing for physics and programming tests. - Developers looking to improve their advanced Python skills. - Researchers and engineers who use Python for complex scientific applications. 🔔 Register now! Don't miss the opportunity to master advanced physics while developing skills in optimized and efficient Python. Join our program "🚀 Advanced Courses - 🔬 Physics from High School to University & 🐍 Advanced Python Programming Focused on Efficiency!" and progress at your own pace with confidence and motivation!

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This course is designed for anyone interested in learning data science using Python. It provides a hands-on introduction to fundamental data analysis tools such as NumPy, pandas, matplotlib, and seaborn. You'll learn how to manipulate datasets, create visualizations, and lay the foundations for statistical analysis and machine learning. The course combines theory and practical exercises for effective, practical progress. No prior programming experience is necessary: we'll start with the basics to build solid, usable skills quickly.

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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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Are you interested in learning Python but don't know where to start? This course is designed to help you understand the fundamentals of Python programming in a simple, practical, and enjoyable way. Python is one of the most popular programming languages in the world. It is widely used in software development, automation, data analysis, artificial intelligence, and web development. Whether you are a complete beginner, a student, or someone looking to develop new technical skills, these lessons will help you build a strong foundation in programming. You will learn 1. Introduction to Python 2. Python Fundamentals 3. Conditional Statements and Loops 4. Working with Data 5. Functions and Practical Programming 6. Mini 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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I teach Python, C and C++ one to one, online or in person around Birmingham. Most of my students fall into one of three groups. Some are at GCSE or A-Level and need to get comfortable with a language before an exam or a coursework deadline. Some are at university, usually on an engineering or computing degree, and have hit something specific that isn't clicking: pointers, memory, recursion, object orientation, or a project that won't compile. And some are adults starting from nothing, often because work has started asking them to automate things. Lessons are built around code you can run. I'll ask what you're working on and where you got stuck, then we write something small together, break it on purpose, and work out what the error message is actually telling you. Reading error messages properly is half of programming and almost nobody teaches it. Areas I cover regularly: Python from the basics through functions, data structures, file handling, object orientation and libraries like NumPy and Pandas C and C++, including the parts that cause most of the trouble: pointers, memory management, structs, classes and compilation GCSE and A-Level Computer Science across all exam boards, including pseudocode, trace tables and written paper technique A-Level NEA projects and university coursework, plus debugging sessions and code review Embedded C for Arduino, ESP32 and microcontroller projects, which is the work I do professionally After each lesson I send written notes covering what we did, worked through step by step, so you have something to revise from later rather than trying to remember what was on screen. First session is free and lasts 30 minutes. We use it to work out what you need and whether I'm the right person for it. If I'm not, I'll say so and point you somewhere better. Message me with what you're studying and what's giving you trouble, and I'll tell you honestly how I'd approach it.