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I teach Python specifically for finance and data applications - the kind used in economics, business analytics, and quantitative programs. This isn't a general "learn to code" course; it's built around real financial data, benchmarking, and the workflows you'll actually use in coursework or early career work. Topics include: Python fundamentals through a finance lens (data structures, functions, control flow). Working with financial data and datasets. Performance benchmarking and writing efficient code. Applying concepts from Hilpisch's Python for Finance.

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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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Understanding algorithms is essential for anyone pursuing computer science or programming. In this course, I provide a structured approach to learning algorithms—from fundamental concepts like sorting and searching to advanced techniques such as dynamic programming and graph theory. Whether you're preparing for exams, coding interviews, or aiming to improve your problem-solving skills, this course is designed to build strong algorithmic thinking. Through practical examples and step-by-step explanations, I help students master the logic and techniques needed to write efficient and elegant code.

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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

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📊Microsoft Office: To establish your office skills, manage data via Excel, write professional reports with Word, and prepare impressive presentations with PowerPoint. 📐 AutoCAD: To learn engineering drawing and design two-dimensional and three-dimensional plans with high accuracy and speed. 🛣️ Civil 3D: To learn road design, network design, surveying, and quantity surveying in a scientific and practical way

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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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Most kids think coding is for "smart kids" or "future programmers." It's not. Coding is how real people solve real problems. In this class, we skip the theory. Your child creates real things. What they'll do: ✓ Build real projects in Scratch: a working game, an interactive animation, a story they coded ✓ Program virtual robots: solve real-world challenges (navigate a maze, automate a task, build a system) ✓ Create in Minecraft Education: design worlds, automate constructions, solve logic problems ✓ Experiment with different languages: not just learn "the right way," but understand that there are many ways to think about a problem ✓ Collaborate and share: work with other kids, get feedback, improve their work ✓ Develop logical thinking: not just for coding, but for anything: solving math problems, science challenges, real-world situations Why this is different: We don't teach syntax. We teach how programmers think. Most children's coding courses say "here's the code, copy it." We teach "what problem are we trying to solve? How could we break it into steps? What options do we have?" When your child learns to think like a programmer, they can learn any language afterward. What they take home: A portfolio of 3–4 completed, working projects. The ability to say "I built this." And the deep understanding that code is a tool to make real things happen. Format: Online or Barcelona | 60–90 min sessions | Flexible pace, no prior experience needed For curious 8-12 year olds who want to build.

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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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Hello, I am a doctoral student in electrical engineering and associate professor in engineering sciences, experienced in the field of electrical engineering, I offer support courses in the subjects of engineering sciences (Electronics, automatics, electrical engineering, automation, programming). Digital electronics Analog electronic electromagnetism (propagation of high frequency waves) Automatic (continuous, sampled) electrical engineering (transformers, electrical machines, switching power supply) C / c ++ programming, Assembler, ARM, STM32 renewable energy (wind, PV) engineering Sciences RDM Python,VHDL PIC Microprocessor and Microcontroller Signal processing and data acquisition Engineering Sciences These courses allow the student to get up to speed and regain confidence in all scientific subjects, just as they prepare him effectively for the Baccalaureate, the Preparatory Classes or various examinations of the engineering classes. COURSE OBJECTIVES AND PEDAGOGICAL APPROACH Resumption and deepening of fundamental concepts through exercises with course reminders. Put the student in a situation of questioning and research. Respond to individual issues and questions Exercise training in order to achieve real mastery of the content. Learn to build theoretical reasoning from observable facts or hypotheses. Specific preparation for higher education requirements (in-depth content, increase in work capacity, enrichment of scientific background) This educational approach is effective since it has often led me to interesting results with my students. Associate professor provides support courses in electrical engineering

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Python is a powerful and versatile programming language with countless possibilities. You can use it for data analysis, image processing, automation, software development, hardware control, and much more. Do you want to create your own software? Work with data or images? Automate repetitive tasks? Control or manage your own hardware? Whether you are just starting to learn Python or already have a specific project and need some guidance, I would be happy to help you. My goal is to explain things clearly, adapt to your level, and help you understand not only how to make something work, but also why it works. Let's turn your ideas into working Python projects!

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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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doctoral student in engineering sciences provides support courses in analog and digital electronics at any DEUG level and engineering schools. having scientific and technical knowledge, three years of experience in the field of teaching, pedagogy and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they are having difficulty. for more info please contact me

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I teach coding to beginners and intermediate students. Lessons focus on logic, basic programming, and practical exercises. Classes are adapted to the student’s pace. Students can choose between website or mobile app development.Hands on dev

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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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Primary School Teacher. I offer personalized guidance in developing lesson plans and learning activities adapted to current educational regulations. During our sessions, you will learn to structure and write a complete lesson plan, including specific competencies, assessment criteria, core knowledge, methodology, addressing diversity, evaluation, and learning situations. My goal is to help you understand each section so you can independently develop a coherent, rigorous, and high-quality lesson plan. You will learn to design practical, creative proposals, developing the necessary skills to apply them in the classroom or in competitive examination processes.

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I offer Microsoft Word, Microsoft Excel, Microsoft PowerPoint, Microsoft Office sessions Word is a word processing software developed by Microsoft. It is used to create documents, letters, reports, presentations... Excel is a spreadsheet software from the Microsoft Office suite developed and distributed by the publisher Microsoft. It is used to create spreadsheets, tables, graphs, etc. Powerpoint is a presentation software developed by Microsoft. It is part of the Microsoft Office suite. It is used to create presentations, slide shows... I am waiting for your message.

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* This digital training aims to introduce you to the Scratch tool and through the game world, and gradually, to discover programming concepts such as loops, conditions or variables. It is aimed at anyone who is new to Scratch and who wants to create games and animations. * Learning programming will allow students to develop their skills and will certainly allow them to meet the expectations of the future working world and emerging careers. * In addition, learning programming allows the development of algebraic, algorithmic and computational thinking. Programming also helps to improve and develop students' sequencing ability, as well as their communication skills. Thus, there are several advantages to teaching programming, but the important thing is to remember that this learning teaches students that digital is not only for entertainment, but that it is possible to become creators. active and creative content.

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With 7 years of experience as a developer in a Factory, I now develop Wordpress websites for large groups. In the training program, you will learn to: - Install, configure, and deploy WordPress sites - Develop custom Wordpress themes and plugins - Use powerful tools like ACF Pro, Gravity Forms, WPML, AIOS Security, WP rocket... - Create custom post types and tailor-made taxonomies - Develop pages using the right methods in HTML, CSS, Javascript & PHP - Administer and manage a site database - Contribute to the good natural referencing (SEO) of your site - Use WP-Cli to speed up the maintenance of your sites - Use Docker to containerize your local/prod projects and facilitate their management - Administer your WP sites the right way - And much more... For any questions, you can contact me by PM. See you soon, Matthew

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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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Python is a simple, powerful, and versatile programming language, ideal for beginners looking to learn or change careers in computer science. This course will help you understand the basics of programming (variables, loops, conditions, functions) through practical, hands-on exercises. You'll learn how to create your first scripts, manipulate data, and automate simple tasks. All in an accessible, interactive, and practical setting. This course is aimed at anyone who is a beginner, curious about digital technology, or who wishes to develop skills useful in many professional sectors.

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I teach machine learning, AI and Python online to university students, postgraduates, career changers and serious beginners across the UK and Europe. Lessons are in English. I hold an MSc in Electronics and Electrical Engineering with Distinction and I teach as a Visiting Lecturer on a Master's level module covering data analytics, machine learning and generative AI at a UK university. I also have two accepted international conference papers on deep learning for image classification. I set and mark postgraduate assignments myself, so I know where marks are won and lost on this kind of work. Who this is for Undergraduates and postgraduates on AI, ML, data science or computer science modules at any European university. Final year, Master's and thesis students working on a machine learning project. IB and A Level students moving into computing or engineering. Professionals retraining for data roles. Complete beginners who want to learn Python properly rather than copying it from videos. I work with students on UK, IB and continental European programmes. I have tutored engineering students in Germany and international school students across several countries, so an unfamiliar syllabus or a module taught in a different structure is not a problem. Send me the material and I will work from it. What we cover Python for data science with NumPy, Pandas, Matplotlib and scikit-learn. Deep learning using TensorFlow and Keras. Core theory including regression, classification, clustering, decision trees, random forests, neural networks and CNNs, together with the linear algebra, calculus and statistics underneath them. Computer vision and image classification, which is my published research area. Model evaluation, overfitting and hyperparameter tuning. Writing machine learning work up to academic standard, covering methodology, results and critical evaluation. How lessons work Send me your module handbook, assignment brief, thesis spec or the code that will not run, and I plan the session around it before we meet. Nothing generic. In the lesson I explain the concept with a worked example, then you take the keyboard while I watch and correct, because you learn far more doing it than watching me do it. You finish with annotated notes and a clear next step, and you can message me between sessions with questions. Practical details Online over Google Meet or Zoom with screen sharing and a shared whiteboard. Sessions run 60 or 90 minutes. I am based in the UK and teach across GMT and Central European time, with evening and weekend slots that suit students anywhere in Europe. Tell me your course, your deadline and exactly where you are stuck, and I will come back with a plan for the first session.