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I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples. Topics include: pyRevit fundamentals and setup Automating repetitive Revit tasks (model checks, plan generation, data extraction) Writing custom scripts for your firm's specific workflows Applying Python automation to real MEP/BIM projects

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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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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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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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Do you need a boost in mathematics to better understand lessons, pass your exams or prepare for a competition? Do you want to learn to program in C, C++, Python or Java to develop skills sought after in the digital world? This comprehensive and personalized academic support program is designed to meet your needs and help you succeed! 💡 Why Choose this Program? This course offers tailor-made support, adapted to your level and your objectives: 🎯 Progress in mathematics by strengthening your foundations and mastering advanced concepts. 💡 Understand theoretical concepts in depth to better apply them in exercises and problems. 💻 Learn to program in C, C++, Python and Java with clear explanations and practical exercises. 🚀 Develop essential skills in algorithms and computer problem solving. 🎓 Effectively prepare for your exams (Bac, Licence, competitive exams) thanks to targeted revisions and practice subjects. With a caring educational approach, this course helps you gain confidence and achieve your academic goals. 📘 Mathematics – From Secondary to Bachelor Mathematics is the key to academic success in many scientific and technical fields. This module covers: Secondary Level (Middle and High School): Arithmetic, fractions, percentages, proportionality. Algebra: Equations, inequalities, functions (linear, quadratic, exponential, logarithmic). Geometry: Theorems, trigonometry, analytical geometry. Statistics and probability: Analyze data, calculate probabilities. Preparation for exams: Brevet, Bac, entrance exams for grandes écoles. University Level (Bachelor): Differential and integral calculus: Derivatives, integrals, sequences and series. Linear Algebra: Matrices, vectors, systems of linear equations. Advanced Probability and Statistics: Random variables, probability laws, estimation and hypothesis testing. Numerical analysis: Methods for approximate resolution of equations and systems of equations. Discrete Mathematics: Graphs, Boolean logic, combinatorics. This module offers progressive exercises, clear explanations and detailed corrections to understand in depth and train effectively. 💻 Programming – C, C++, Python and Java Mastering programming is a major asset for success in the digital and technological field. This module covers the fundamentals of programming to enable you to: Understand algorithmic logic and computer problem solving. Master the syntax of the C, C++, Python and Java languages. Writing your first programs: Variables, conditional structures, loops, functions. Work on practical projects: Calculator, data management, simple games, sorting and searching algorithms. Develop advanced skills: Object-oriented programming (C++, Java): Classes, inheritance, polymorphism. Memory management (C, C++): Dynamic allocation, pointers. File manipulation: Reading and writing data. Data structures: Lists, stacks, queues, binary trees. Code optimization for faster and more efficient programs. This module offers concrete examples, practical exercises and motivating projects to help you learn while having fun while developing skills useful in the professional world. 🎯 Interactive and Motivating Teaching Dynamic online courses: Learn from home in an interactive format with audio and screen sharing. Tailor-made method: The courses are designed according to your level and your objectives for learning at your own pace. Practical exercises and concrete projects: To apply theoretical concepts and develop your skills. Personalized monitoring: Regular support to monitor your progress and adapt the program to your needs. Encouragement and motivation: A positive approach to building your confidence in your abilities. 🔔 For Who? This program is aimed at: High school students wishing to strengthen their foundations in mathematics or learn to program. University students in science or computer science looking to deepen their knowledge of math and programming. Candidates for exams and competitions preparing for the Baccalaureate, a License, or entrance exams to the grandes écoles. Programming enthusiasts wanting to learn the fundamentals of C, C++, Python or Java. Adults in professional retraining wishing to acquire programming skills. 🚀 Ready to Succeed? Join the "🎓 Academic Support – 📘 Maths from Secondary to Bachelor & 💻 Programming in C, C++, Python and Java!" and benefit from personalized support to achieve your goals. Whether you want to improve your grades, pass your exams or develop programming skills, this program will give you knowledge, confidence and motivation. Register today and take the first step towards success!

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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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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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In today's rapidly evolving technological landscape, **Python programming** has emerged as one of the most **critical skill sets** for professionals across industries. With applications spanning web development, data science, artificial intelligence, automation, and more, Python continues to dominate as the **language of choice** for developers and organizations worldwide. This proposal outlines a comprehensive Python course designed and delivered by **Amr**, a developer and instructor with over **20 years of experience** in the field. The course combines fundamental programming concepts with practical, real-world applications, ensuring students gain not just theoretical knowledge but **marketable skills** that align with current industry demands. By leveraging cutting-edge teaching methodologies and extensive professional experience, this course offers an unparalleled learning opportunity for aspiring programmers and experienced developers alike. ## 1 Introduction to Python Programming Python has established itself as a **powerhouse programming language** across various domains, from web development and data analysis to artificial intelligence and automation. As of 2025, the demand for Python skills continues to soar, with industry giants like Cisco, IBM, and Google leveraging its capabilities for their projects . Python's dominance in the technology sector is undeniable – it remains the **most requested programming language** in job postings across multiple industries, including finance, healthcare, technology, and entertainment. The language's popularity stems from several key factors: its **user-friendly syntax** that resembles natural English, making it exceptionally accessible for beginners; its **versatile nature** that supports multiple programming paradigms; and its **extensive ecosystem** of libraries and frameworks that simplify complex programming tasks. Python's cross-platform compatibility ensures code runs seamlessly on Windows, macOS, and Linux environments, while its open-source nature has fostered a massive community of contributors who continuously expand its capabilities . These attributes make Python not just a programming language but a **comprehensive toolset** for solving diverse computational problems. For professionals looking to future-proof their careers, Python offers **exceptional value**. According to industry data, Python developers in the United States earn an average of **$116,028 per year**, reflecting the high market demand for these skills . Beyond financial rewards, Python proficiency opens doors to cutting-edge fields like machine learning, natural language processing, and data analytics – domains that are shaping the future of technology across industries. ## 2 Course Overview & Learning Objectives ### 2.1 Course Philosophy This Python programming course is designed with a **practice-oriented approach** that emphasizes hands-on learning and real-world application. Unlike traditional programming courses that focus heavily on theory, this program balances conceptual understanding with **practical implementation**, ensuring students develop the skills needed to solve actual business problems. The curriculum is structured to build proficiency gradually, starting with fundamental concepts and progressing to advanced applications, with each module incorporating **project-based learning** components. ### 2.2 Key Learning Objectives Upon successful completion of this course, students will be able to: - **Demonstrate proficiency** in core Python programming concepts including data structures, control flow, functions, and file handling - **Develop functional applications** using Python for various domains including web development, data analysis, and automation - **Implement object-oriented programming** principles to create modular, maintainable code - **Utilize popular Python libraries** such as Pandas, NumPy, and BeautifulSoup for specialized tasks - **Integrate with databases** and web APIs to create full-stack applications - **Apply debugging and testing** techniques to ensure code quality and reliability - **Build portfolio-worthy projects** that demonstrate marketable skills to potential employers ## 3 Instructor Qualifications & Experience ### 3.1 Professional Background **Amr** brings an exceptional **twenty-year track record** of development and instruction experience to this Python course. His extensive background encompasses both corporate training and software development, providing a unique blend of pedagogical expertise and practical knowledge. With credentials including a **Bachelor of Computer Science and Management Technology** from Modern Academy and a **Computer Science Diploma** from Arab Academy for Science and Technology, Amr possesses the academic foundation to complement his extensive professional experience. His career demonstrates **progressive responsibility** and expertise across multiple programming languages and frameworks. Beginning as a technical instructor at renowned institutions including NewHorizons, Knowlogy, and Informatica, he quickly established himself as a developer at Microtech and ITS, where he worked on enterprise-level systems including **ERP and banking applications**. This combination of education and hands-on development experience creates an ideal foundation for teaching programming concepts with both theoretical rigor and practical relevance. ### 3.2 Industry Client Portfolio Amr's exceptional teaching credentials are further enhanced by his impressive roster of **corporate clients**, which includes some of the world's most recognized brands: - **Technology Leaders**: Microsoft, IBM, Siemens, Vodafone, and Telecom Egypt - **Financial Institutions**: National Bank of Egypt, NSGB, CIB, and Central Bank of Egypt - **Global Consumer Brands**: Pepsi, Coca-Cola, Nestlé, Cadbury, and Americana - **Industrial Conglomerates**: Chrysler, Valeo, 3M, ABB, and BP (British Petroleum) - **Government Entities**: Libya Government IT Department, Sudan Army Officers, Egyptian Airports Company This diverse client experience has provided Amr with **unparalleled insight** into how Python is applied across different industries and organizational contexts. His exposure to various business domains allows him to teach Python not as an abstract academic exercise but as a **practical tool** for solving real business problems. ### 3.3 Teaching Methodology Amr employs a **learner-centered approach** that emphasizes interactive engagement and practical application. His teaching philosophy is based on the principle that programming is best learned through doing, rather than passive listening. Each concept is introduced through **clear explanations** followed immediately by hands-on exercises that reinforce learning. He adapts his pace and approach based on student comprehension, ensuring no one is left behind while maintaining challenging content for advanced learners. *Table: Instructor's Recent Training Engagements (2023-2025)* | **Year** | **Corporate Clients** | **Training Centers** | **Technologies Covered** | |----------|-----------------------|----------------------|--------------------------| | **2023** | International Finance Corporation, Raya Integration | Raya Academy, IT-Egypt | VBA, Office Automation, Web Technologies, Software Fundamentals with C#, SQL Server Database Design and Querying, Introduction to .NET Core Framework, Building ASP.NET Core Web API, Front-End Development Basics (HTML, CSS, JavaScript, TypeScript), Advanced Front-End Development with Angular, Integration and Deployment | | **2024** | 3M, Pepsi | NewHorizons, Radio & Television Institute, Informatics (Lebanon), Total-Tech (KSA), Global Business Star (USA) | SQL Query (20761), SQL Development (20762), SQL Admin (20764,20765), Tabular, MQL5, ASP.NET Core MVC Web Applications (20486), Programming in C# (20483), Programming in HTML5 with JavaScript and CSS3 (20480), LINQ, EF (Entity Framework) | | **2025** | Siemens, Vodafone | YAT, Future University | Full Stack Development, Data Analysis | ## 4 Detailed Course Curriculum ### 4.1 Module Breakdown The Python course is structured into **eight comprehensive modules** that systematically build programming proficiency from foundation to advanced application: 1. **Python Fundamentals** (10 hours): Syntax, variables, data types, operators, and basic input/output operations. Students will write their first programs and understand how Python interprets and executes code. 2. **Control Structures & Functions** (15 hours): Conditional statements (if/elif/else), loops (for/while), function definition, parameters, return values, and scope. Emphasis on writing clean, reusable code. 3. **Data Structures** (20 hours): Lists, tuples, dictionaries, sets, and their appropriate applications. Includes comprehensive exercises on data manipulation and storage. 4. **Object-Oriented Programming** (20 hours): Classes, objects, inheritance, polymorphism, and encapsulation. Students will learn to structure code using OOP principles for better maintainability. 5. **File Handling & Modules** (10 hours): Reading/writing files, exception handling, importing modules, and creating custom modules. Practical applications for data persistence. 6. **Web Development with Python** (25 hours): Introduction to Flask/Django frameworks, REST APIs, and basic front-end integration. Students will build a functional web application. 7. **Data Analysis & Visualization** (25 hours): Using Pandas for data manipulation, NumPy for numerical computing, and Matplotlib/Seaborn for visualization. Real-world datasets will be used for analysis. 8. **Introduction to Automation & Scripting** (15 hours): Applying Python to automate repetitive tasks, web scraping with BeautifulSoup, and working with APIs. ### 4.2 Practical Projects The curriculum includes **five portfolio projects** that allow students to apply their learning: 1. **Data Analysis Project**: Analyzing real business data to extract insights and create visualizations 2. **Web Application Project**: Building a fully functional web application with database integration 3. **Automation Script**: Creating a practical tool to automate a repetitive computer task 4. **API Integration Project**: Connecting to external services and processing returned data 5. **Final Capstone Project**: A comprehensive application that demonstrates mastery of course concepts ### 4.3 Python in Marketing Analytics A special section of the course will focus on **Python applications in digital marketing**, covering how Python can be used for marketing automation, data analysis, and operations . Students will learn: - **Working with APIs** to connect different software tools and automate marketing workflows - **Web scraping** to gather data from web pages for content analysis and competitive intelligence - **Text analysis** for sentiment analysis, content optimization, and customer feedback processing - **Data analysis** for marketing analytics using Pandas and visualization libraries - **Technical SEO** applications using Python libraries like advertools and EcommerceTools This specialized content demonstrates Python's versatility beyond traditional programming roles, showing its value in business functions like marketing where data skills are increasingly crucial. ## 5 Training Methodology & Delivery ### 5.1 Interactive Learning Approach This Python course employs a **multimodal teaching methodology** that accommodates diverse learning styles while ensuring practical skill development. Each session follows a structured pattern: 1. **Concept Introduction**: Clear explanation of programming concepts with real-world analogies 2. **Live Coding Demonstration**: Step-by-step coding examples that students can follow along 3. **Guided Practice**: Structured exercises with instructor support and immediate feedback 4. **Independent Challenge**: Problem-solving activities that require applying concepts creatively 5. **Code Review**: Collaborative analysis of solutions to identify best practices and improvements This approach ensures that students not only understand theoretical concepts but develop the **problem-solving mindset** essential for effective programming. The emphasis is always on writing clean, efficient, and maintainable code following industry standards. ### 5.2 Hands-On Labs & Exercises A distinctive feature of this course is the extensive **hands-on programming practice** integrated throughout the curriculum. Students will spend approximately **60% of course time** actively writing code rather than passively listening to lectures. Practical components include: - **Coding exercises** for each new concept introduced - **Mini-projects** that combine multiple concepts into functional applications - **Debugging challenges** that develop problem-solving skills - **Code optimization** activities focusing on efficiency and performance - **Pair programming** sessions to foster collaboration and knowledge sharing ِSend me if you have any questions, Regars, Amr

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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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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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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.