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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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Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance. Whether you've never written a line of code or you're a student who needs to pass a programming course, this is a practical, no-fluff introduction that gets you writing real code from session one. What we can cover depending on your goals: Python fundamentals: variables, loops, functions, data structures - Object-oriented programming (OOP) - Data manipulation with pandas and NumPy - Introduction to machine learning with scikit-learn - Database management with SQL - C and Java upon request - MATLAB and R available for engineering/science students Why learn with me? I'm not a student teaching on the side — I'm a professional engineer who uses Python daily for data analysis, modeling, and automation. I know exactly which concepts matter in the real world and which ones you can skip for now. Sessions are 100% personalized: I adapt the pace, the examples, and the exercises to your background and your goal — whether that's passing your university exam, building a project, or landing a job.

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If you’ve ever felt that science and math are difficult, it’s probably because no one showed you how to think like a problem solver. In my classes, you’ll learn not just formulas or code but how to truly understand concepts, apply them, and build strong logical intuition. I teach: • 🔢 Mathematics: From algebra and calculus to applied problem-solving for real-world use. • 💻 Computer Science: Coding fundamentals (Python, C++), algorithms, and logical thinking for beginners and intermediate learners. • ⚛️ Physics: Mechanics, thermodynamics, and practical examples that make abstract ideas simple and visual. As a Software Engineer and Master’s student in Engineering at Nagoya University, I bring both academic knowledge and hands-on experience from real projects. My teaching approach is interactive, visual, and deeply focused on understanding over memorization. Let’s turn complex problems into clear, step-by-step insights — and make learning something you genuinely enjoy.

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You will learn Systematic Reasoning & Logical Thinking which is a requirement for entering Computer Science program in many universities. The book “Delftse Foundations of Computation” especially its second chapter will be the main source of our lesson, but other more in-depth books will be also covered if you want to improve even further on logical thinking. The topics in our lesson include: • Propositional Logic: Logical operators; Precedence rules; Logical equivalence; Implications in English; Exclusive or; Universal operators; Classifying propositions • Boolean Algebra: Substitution laws • Logic Circuits: Logic gates; Combining gates to create circuits; From circuits to propositions; Disjunctive Normal Form; Binary addition. • Predicate Logic: Predicates; Quantifiers; Tarski’s world and formal structures; • Deduction: Valid arguments and proofs; Proofs in predicate logic If you have any additional questions before starting a class, please feel free to ask me. I am here to assist! :)

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As a Franco-Belgian management teacher, I give Excel lessons with passion! Whether remotely or face-to-face, I offer many examples and exercises to accompany you. I travel without problem throughout the region of Brussels and its surroundings, for lessons of at least 2 hours. For France, courses are only given remotely. Here are some key words that will be covered in my classes: Scenario analysis, Year, Rounding, Today, Bdnb, Bdnbval, Bdsum, Search, Column, Copy/paste in values, Copy/paste with transposition, Consolidation, Date, Datedif, Determat, Dollar, Right, Righterg, Equiv, Esterror, Estna, Frequency, Filter (simple and advanced), Format of cells, Left, Large.Value, Printing of documents, Index, Indirect, Inversemat, Day, Weekday, Line, Matrix, Max, Maxa, Max.Si, Min , Mina, Mina.If, Formatting of cells and ranges, Month, Average, Average.If, Nb, Nb.If, Nbval, Naming of cells and ranges, No, Small.value, Product, Productmat, Protection of cells, Lookup (Lookup), Lookupv (VLookup), Lookuph (HLookup), If (If), If.Not.Disp, If.Conditions, Iferror, Sum, Sumproduct, Sum.If, Sum.If.Set, Substitute , Pivot tables, Sorting, Cell locking Do not hesitate to contact me to organize your lessons according to your needs and availability. Together, we will develop your Excel skills in an efficient and personalized way.

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With over seven years of experience in teaching Computer Science & Information Technology (ICT), I have developed a strong expertise in delivering high-quality education across multiple internationally recognized curricula, including Cambridge IGCSE, GCSE, A-Levels, O-Levels, and Checkpoint. My passion lies in equipping students with coding, cybersecurity, and digital literacy skills, ensuring they are well-prepared for the evolving demands of the digital world. Expertise & Teaching Areas: ✅ Programming & Software Development: Python, Java, C++ ✅ Cybersecurity: Ethical hacking, data protection, network security ✅ Digital Literacy: ICT applications, online safety, cloud computing ✅ Data Science & AI: Data analysis, machine learning fundamentals ✅ Web Development: HTML, CSS, JavaScript Curriculum & Pedagogical Experience: 🔹 Cambridge IGCSE & GCSE ICT & Computer Science – Teaching core and extended syllabi, focusing on programming logic, databases, and networking. 🔹 Cambridge A-Levels & O-Levels Computer Science – Preparing students for advanced computing concepts, problem-solving, and algorithm development. 🔹 Cambridge Checkpoint ICT – Building foundational skills in digital technology and computer applications. Professional Impact: 📌 Mentored students to achieve top grades in Cambridge ICT & Computer Science exams. 📌 Developed interactive lesson plans integrating real-world applications of technology. 📌 Conducted coding boot camps and cybersecurity workshops to enhance practical learning. 📌 Guided students in project-based learning, including app development and website design. With a strong commitment to student-centered learning and technological innovation, I am dedicated to shaping future tech leaders and empowering learners with skills relevant to careers in technology, data science, and software development.

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This course introduces students to the fundamentals of Information and Communication Technology (ICT) and its role in modern society. Topics include computer hardware and software, digital communication tools, internet technologies, data management, cybersecurity, and emerging trends. Students will gain practical skills in using productivity software, conducting online research, and understanding the ethical and responsible use of digital resources. The course emphasizes both technical proficiency and digital literacy, preparing learners to confidently navigate and contribute to a technology-driven world.

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Whether you're preparing for IB, IGCSE, GCSE, or simply want to build confidence in Mathematics, I'm here to help. I have over 3 years of teaching experience and 7 years of professional software engineering experience, allowing me to explain mathematical concepts in a clear, logical, and practical way. My lessons are tailored to each student's needs and learning pace. I believe understanding the "why" behind a concept is far more valuable than memorising formulas. During lessons, I use carefully prepared practice questions and exam-style exercises, many of which I create myself using professional typesetting tools like LaTeX that is used by IB and Cambridge directly. I encourage students to ask questions freely and develop strong problem-solving skills. Whether you're aiming to improve your grades, prepare for school exams, or study for international curricula such as IB, IGCSE, or GCSE, I'll help you build confidence step by step in a supportive learning environment. I use a Pen Display Tablet to write on it using Goodnotes as the writing app, which I share the link to the whiteboard used with the students. Lessons are available online or in-person (within 30 mins train travel from Shinjuku, Tokyo), and are conducted in English.

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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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I am a certified computer science professor who helps graduates and students with exam retakes and competitions. I tutor preparatory classes (MPSI, MP, PSI, ECS, etc.) up to university level (Bachelor's & Master's in Science or Economics). My method is based on understanding the lessons, practicing correctly, organizing the concepts, and completing exercises and problems of your choice. Each session includes verbal exercises, methodological tips, and subsequent personalized advice. You will receive a video recording and an annotation in PDF format after each session. The online courses are conducted via Google Meet, 5 days a week, with flexible scheduling. I am available between sessions to answer questions. Contact me for an initial consultation.

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This course provides a foundational understanding of Information Technology, data centers, covering architecture, power & cooling, networking, storage, virtualization, security and lots more. Learn best practices for efficiency, scalability, and reliability while exploring emerging data center solutions. Ideal for IT professionals, engineers, and facility managers involved in data center deployment or management. This course offers a comprehensive exploration of Information Technology, data center infrastructure, guiding students through the entire lifecycle—from initial design and planning to day-to-day operations and long-term performance optimization. Students will learn the critical components of data center design, including site selection, power and cooling systems, space planning, networking, and physical security. The course also covers operational best practices, monitoring tools, energy efficiency strategies, disaster recovery planning, and emerging trends. By integrating technical, environmental, and management perspectives, students will gain the knowledge and skills required to build and maintain high-performance, cost-effective, and sustainable data center environments.

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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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Ever wondered what really happens when you open a website or hit "send"? Let's find out together. I've spent 3 years working hands-on with real networks: analyzing live traffic, managing firewalls, and troubleshooting network issues for government, financial, and telecom organizations. I'm Fortinet certified (FCA, FCF) and hold the CNSP (Certified Network Security Practitioner) certification. I'll teach you networking the way it's actually used on the job. No boring theory dumps, just live, hands-on learning you can use right away. You'll learn: How the internet works: IP addresses, DNS, DHCP, and HTTP/HTTPS The OSI and TCP/IP models, made simple Subnetting, routing, switches, and firewalls TCP vs. UDP, ports, and how connections work How to read real traffic with Wireshark and test networks with ping, traceroute, and Nmap How to spot suspicious traffic and keep a network healthy Perfect for: beginners, career changers, students, developers, and anyone preparing for Network+ or CCNA. You'll walk away with a clear picture of how data travels, the confidence to troubleshoot real network problems, and a rock-solid foundation for your IT career. No experience needed, just curiosity and a computer. Book your first session and let's dive in!

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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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As a Master's student in Data Science at EPFL, a graduate of CentraleSupélec (ranked in the top 3% of my class) and holder of a Bachelor's degree in microtechnology from EPFL, I offer tutoring in mathematics, physics and computer science, from primary school to university level. My teaching experience I was a student teaching assistant at EPFL for 8 courses, working with over 400 students. I currently lead the linear algebra and ICC (Information, Computation, Communication) exercise sessions. Each week, I adapt my explanations to each student's level: that's what I love most about teaching. What I propose • Primary and secondary school: consolidate the basics (calculation, fractions, geometry, equations), regain confidence and improve methodology. • Gymnasium / high school (maturity, baccalaureate): functions, analysis, probabilities, vectors, mechanics, electricity, exam preparation. • University / EPF / preparatory classes: analysis, linear algebra, probability and statistics, numerical analysis, programming (Python, C/C++). My method I begin by identifying the real obstacle: a misunderstanding of the concept, a lack of methodology, or stress. Then, I build the sessions based on the student's lessons and exercises. The goal isn't just to pass the next test, but to understand the material and become independent. Whether you need occasional homework help, regular support, or intensive exam preparation, I adapt to your needs.

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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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This computer science support course is designed for students and learners wishing to strengthen their foundations or improve their level in computer science and programming. I support participants in a pedagogical and progressive manner, adapting to their level and objectives (university courses, training, practical work, exams, projects). The goal is to understand, practice and gain autonomy through clear explanations and concrete examples.

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Your application is running on your machine... and then what? Knowing how to containerize and automate deployments has become an expected skill for every developer, and it's often what junior candidates lack during interviews. I'm a full-stack engineer and I implement these pipelines in production (Docker, GitLab CI/CD, GitHub Actions, GCP Cloud Run, Kubernetes). I'll teach you how to do the same, on your own project. Program (adapted to your level): Docker basics: images, containers, Dockerfiles, volumes, networks Docker Compose: Launch a complete app locally (front end + API + PostgreSQL database) Optimizing your images: multi-stage builds, lightweight images, security best practices Continuous integration: your first pipeline with GitHub Actions or GitLab CI Automatic quality control: tests, linting, and formatting are performed with each push. Build & registry: build and publish your images (Docker Hub, GCP Artifact Registry) Continuous deployment: put your app online in the cloud (GCP Cloud Run), manage environment variables and secrets Final project: a complete pipeline, from git push to online application, including database migrations For who ? Developers (Node.js, React, Python, PHP...) who want to master deployment Students and career changers who want a solid project for their portfolio Teams or freelancers who want to automate their production deployments Prerequisites: knowledge of how to use Git and having already coded a small application (in any language). Each session is practical: we work directly on your code, and you leave with a working pipeline. Feel free to contact me to discuss it!

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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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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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I created this course especially for students having difficulty progressing in their computer programming courses/projects. I support students of all university levels. My help covers many others: - Analysis and planning of projects - Technological choice in languages and tools/framework - Algorithmic and programming support - Assistance with debugging and code correction - Data modeling (MCD/MLD diagram, UML diagrams) Why choose my courses? My method is different and more adapted than that of traditional teachers because it is: * Personalized: Adapted to your level and your specific needs. * Interactive: Promotes interaction and visualization of concepts. * Practical: Oriented towards practice with concrete exercises and projects. * Proven: I have already managed to help more than 200 students in the space of 2 years.

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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.