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Python is a programming language with a simple syntax, similar to the English language. It allows developers to write programs with fewer lines than some other programming languages. It can be used in many areas such as web development, mathematics, science, education and general purpose software development. This course aims to demonstrate how Python can be used to create solutions to real-life problems or programs that can be used to faciltate tasks performed at school, in a workplace or at home. It aims to engage not only learners with advanced programming experience but also those who have little or not any experience at all. Creative and critical thinking are continuously encouraged during the lessons. Relevant topics: - Input/Output - Data types and variables - Operators - Lists,tuples, sets - If / else - Loops - Functions - Lambda - Classes/Objects - Inheritance - Iterators - Scope - File handling - Modules - NumPy - Python Maplotlib - Python SciPy The content and pace of the course is adapted to the age and needs of the learners. Younger learners may prefer to start with my course entitled “Create fun games and interactive programs in Python”.

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Knowing how to program is an asset for everyone, regardless of your field of work. Whether it's to automate repetitive tasks, assist with calculations or data analysis, develop applications, or simply for enjoyment, everyone benefits from a basic knowledge of programming. With a decade of experience in the field and advanced computer training as part of my studies, I offer to help you discover the wonderful world of programming, or improve your skills with a personalized approach, tailored to your objectives, including theoretical parts and exercises.

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Experienced teacher with a great passion for teaching. My background is in both engineering and management fields with one bachelor's and two master's degrees. I have a good background in Python programming and Microsoft office (Word, Excel, PowerPoint, Access,...) and statistics. I am also able to teach online if you would like. You will receive full support during and after each course with high quality and minimum price. For University/High school/Primary school students.

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I offer private computer lessons regardless of your level. With a bachelor's and master's degree in Computer Science, I have always enjoyed sharing my knowledge with my students. I help you solve your exercises, understand and apply the theory, and complete projects.

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Graduated from a French engineering school (ENSTA) and holder of a master's degree in probability from the University of Paris VI, I can assist you in learning Python, from the basics to the deepening, through school support and upgrading / exam preparation. And this for: - programming in general, - advanced algorithms - datascience (including machine learning). I hope to see you soon !

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Are you having trouble with your programming classes? Have you always been intrigued by programming, but don't you dare take the plunge on your own? Do you already have basic knowledge, but want to improve your level? So I can help you with your goals. I adapt to your individual needs and experience level. With 10 years of scientific research and holder of a doctorate in Geographical Sciences from the Free University of Brussels, I have been using Python for several years for the automation of data collection and processing, in particular the processing of satellite images with Machine Learning and Deep Learning tools. Why Python and not another programming language? • Among the various programming languages, it is probably the one with the simplest and easiest to understand syntax, which makes it an ideal language for beginners. • It is one of the most popular programming languages and the demand for professionals with Python skills is high. Many industries, including technology, finance, and healthcare use Python extensively. • It is a versatile language that can be used for many applications like scientific computing, data analysis, artificial intelligence, etc. Different formulas are possible: • Help with success for students having difficulties with their programming courses. • For people with basic knowledge of Python: ◦ Take it to the next level with object-oriented programming. ◦ Introduction to the highly sought-after field of Data Science Data with Python

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1Students

Programming is a handy skill that any person can pick up at any rate and age. It allows us to think in abstract terms writing concise instructions to a computer to perform a certain task: be it a basic addition to more advanced software engineering. As a graduate of the Free University Brussels, I put my skills into good use to teach people about programming in order to give them the required insights to succeed in the domain. My skill set is in Python, Java and Javascript with a good understanding in C.

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At today's world software development is an integral part of one's skill. I invite you to this class where Python programming languages is explained in an easy to understand way with lot of hands on exercises and sample projects. You will feel the difference in just few classes.

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19Students

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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1Students

I offer one-to-one Programming tuition in Python, C, and C++, for GCSE Computer Science, A-Level Computing, and university students studying engineering, computer science, or related subjects. Lessons are available online or in person around Birmingham. What I cover: Python for beginners and intermediate learners C and C++ programming GCSE and A-Level Computer Science (all exam boards) University coursework support, debugging help, and project guidance Core concepts: variables, loops, functions, data structures, object-oriented programming, file handling, basic algorithms How I teach: I start by understanding exactly where you are — whether that's "I've never coded before" or "I'm stuck on a specific assignment." Then I build lessons around small, practical examples you can actually run and modify yourself. I'm patient with errors (everyone gets them), and I make sure you understand the why behind the code, not just how to copy it. For university students, I can also help with debugging, code reviews, and explaining tricky concepts in plain English. If you or your child is preparing for exams, working on coursework, or just wants to finally feel comfortable with coding, I'd love to help.

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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- 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 3- 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 4- 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 5- 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 6- 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 7- 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 8- 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 9- 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 10- 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 11- 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 free 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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3Students

Python is a very powerful and multi purpose tool. Image analysis, data analysis. Do you want to create your own software? Do you want to manage your own hardware? For who may want to learn Python or need help for a project I would gladly help you with that!