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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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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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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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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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This course is designed for students and adolescents who want to build a strong beginner-friendly foundation in Computer Science. Whether you're completely new to Computer Science or need help with a specific programming subject, the course can be customized to match your needs. Need help with C++ or programming? You can provide me with your syllabus, course outline, or the topics you're studying, and I'll tailor the lessons around what you need to learn. Learning with friends? Group lessons are also available, allowing you to learn together while following the same customized course.

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🐍 Python Course – Learn to code and create your projects! This course is for anyone who wants to: ✅ Learn Python from the beginning ✅ Strengthen their programming skills 📚 On the program: Variables Loops Functions Data structures Practical projects for implementation 💡 How does the course work? Clear explanations to understand the programming logic Targeted exercises adapted to your level Concrete projects to create your own applications 🎯 My goal: Helping you understand the logic behind the code Progress at your own pace Create your own projects in Python and gain independence

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Discover programming lessons suitable for children! With a fun and educational approach, my lessons allow young minds to dive into the fascinating world of programming. Provide your children with an enriching learning opportunity in a fun and stimulating environment.

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You know how to write Python scripts, but your code quickly becomes difficult to read and maintain? Object-oriented programming is the most common way to organize a program, and it's essential in business, engineering school, and technical interviews. I'm a software engineer and I use Python in production (FastAPI, data, optimization). I'll teach you OOP in a practical way: using classes when they simplify the code, and knowing when to do without them when they complicate it. Program (adapted to your level): Clean code: why programming paradigms exist and how they make code maintainable Classes and objects: attributes, methods, the `__init__` constructor, `self`. Differences between OOP and the functional approach Encapsulation: protecting an object's data, private attributes, properties (@property) Abstraction: hiding complexity and exposing a simple interface. Abstract classes (abc) Inheritance: reusing and specializing class behavior, super(), when to avoid it Polymorphism: the same call, different behaviors. Special methods (__str__, __eq__...) Final project: model a real problem from A to Z (game, library management, reservation system...) with a code organized into classes For who ? Students (BTS, BUT, Bachelor's degree, engineering school, preparatory classes) preparing for an exam or project; High school students Career changers and junior developers who want to take their skills to the next level Prerequisite: knowledge of the basics of Python (variables, conditions, loops, functions). Otherwise, a refresher course can be started. Each session alternates between short explanations and practical exercises. I can also help you directly with your practical work and projects. Feel free to contact me to discuss it!

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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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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 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.