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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.

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I am a Cambridge graduate in CS, with over 10 medals and national distinctions for competitive programming. For over 5 years, I have helped hundreds of students discover how easy and beautiful Computer Science is: - Assisted students in improving from a C to an A* grade. - Helped motivated students qualify for the National Informatics Olympiad. - Guided many achieve their target grades in A-Levels, even attaining the perfect score. - Prepared for IB/IA, A-Levels, GCSE, University Entry, or equivalent. - Assisted with specific projects at a professional level, including interview preparation. All my success in Computer Science is due to my teachers, who knew how to inspire my passion and turn any concept into an easy-to-understand story. That's why I decided to help others further to elucidate the secrets of Computer Science and to smile with relief in the exam hall when they notice that they know how to solve all the subjects perfectly. It's amazing how quickly a student can progress when the material is explained to them in their understanding. 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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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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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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11Students

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! :)