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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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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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Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty. 1: Demystifying AI (What exactly is it?) AI is not a movie robot: Difference between fiction and reality. How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image. Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa). 2: Using AI to make life easier Conversing with AI (ChatGPT, Claude, Gemini): Ask him to write an administrative email or a complex letter. Summarize a long newspaper article or document. Plan a travel itinerary or find recipe ideas with what's left in the fridge. AI for creativity and memory: Generate images to illustrate a birthday card (Midjourney, DALL-E). Using AI to restore or colorize old family photos. 3: Learning to "talk" to AI (The Art of the Prompt) The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe". The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener". 4: Precautions and Critical Thinking (The Survival Guide) "Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification). Privacy protection: Never give sensitive data (social security number, passwords, bank details) to an AI. Knowing that everything we write to the AI is potentially used to train it. Spotting "Deepfakes": How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice). Verify the information: the golden rule of cross-referencing sources. 5: Ethics and Impacts (To go further) Copyright: Who owns an image created by AI? The environmental impact: The water and energy consumption of AI servers. The future: Will AI replace us or assist us?

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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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📊Microsoft Office: To establish your office skills, manage data via Excel, write professional reports with Word, and prepare impressive presentations with PowerPoint. 📐 AutoCAD: To learn engineering drawing and design two-dimensional and three-dimensional plans with high accuracy and speed. 🛣️ Civil 3D: To learn road design, network design, surveying, and quantity surveying in a scientific and practical way

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Experienced and patient teacher of logic for computer science. I have taught logic, formal languages and automata theory to undergraduates for six years. My tutoring is adapted to the student's level and goals. Whether you need to learn logic for your studies, or you would simply like to know more about the subject, I will be more than happy to help you improve your understanding and skills. Logic The sciences presuppose a certain standard of rationality. An ability to distinguish between correct reasoning and claims that do not follow from the assumptions. In this class we study the basic principles of logic and apply mathematical techniques to the study thereof. Topics include: Propositional and Predicate Logic Syntax and semantics Natural deduction Semantic tableaux Correctness and soundness Completeness Formal languages and automata A formal language is an abstraction of general characteristics of programming languages. Such a languages consists of a set of symbols together with some rules to determine whether a string made up out of those symbols is a member of the language. Topics include: Regular languages, context-free languages Finite automata, pushdown automata, Turing machines Regular expressions Regular grammar, context-sensitive grammar Pumping lemmas for regular and context-free languages

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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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Session 1: Revolutionizing your Scientific Writing with LaTeX & AI Duration: 2 Hours | Level: Beginner | Tools: Overleaf + AI** First Hour: Foundations and Cloud Environment (60 min) 1. Introduction to LaTeX Philosophy (15 min) - The "WYSIWYM" concept:** Explain the difference between Word (*What You See Is What You Get*) and LaTeX (*What You See Is What You Mean*). Why content takes precedence over form. - Key advantages:** Unrivaled typographic quality, automatic reference management, stability on long documents (theses), and free of charge. - The structure of a file:** Distinction between the **preamble** (the brain: settings and packages) and the **body of the document** (the heart: text). 2. Immersion in Overleaf (25 min) - Configuration:** Creation of an account and first project "Blank Project". - Exploring the interface:** The file panel (left), the code editor (middle) and the PDF preview (right). - Real-time collaboration:** How to share a project and leave comments (like on Google Docs). - History and versions:** How to revert to a previous version in case of a compilation error. 3. Practical Workshop: My First Document (20 min) * Writing basic commands: `\documentclass`, `\usepackage[french]{babel}`, `\title`, `\author`. * Compilation of the document and observation of the result. * Structuring: Use of `\section` and `\subsection`. Second Hour: Mathematics and the Magic of AI (60 min) 4. The Power of Mathematics (20 min) - Mathematical modes:** Difference between the text (`$...$`) and the centered block (`\[...\]`). - Essential syntax:** Fractions `\frac{}{}`, exponents `^`, indices `_`, and roots `\sqrt{}`. - Introduction to AMS packages: Why amsmath and amssymb are essential for professional rendering. 5. From hand to screen: AI at the service of LaTeX (30 min) - Presentation of OCR tools:** Use of **Mathpix Snip** (the leader) or models like Gemini/ChatGPT to transform a photo into code. - Concrete demonstration: 1. Take a picture of a complex handwritten formula (e.g., an integral with matrices). 2. Use AI to generate the corresponding LaTeX code. 3. Correction and insertion: Learn to check the AI-generated code before copying and pasting it into Overleaf. 6. Conclusion and Q&A (10 min) * Summary of achievements. * Resources for further exploration * Definition of the exercise for the next session.

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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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This course is aimed at non-Arabic speakers wishing to learn Arabic in a simple, progressive and practical way, while discovering Moroccan pronunciation (darija). It is accessible to all beginner levels, and requires no prior knowledge. Through an interactive method, you will learn: The basics of the Arabic alphabet and correct pronunciation. Essential expressions for everyday communication. The construction of simple sentences (present, past, future). Useful vocabulary: family, food, time, travel, emotions... An introduction to Moroccan Darija, to understand and speak easily with Moroccans. The course is suitable for: Adults, students and travellers. People wishing to discover Moroccan culture. Learners seeking patient, clear and motivating support. I provide : Structured course materials (PDF + exercises). Audio recordings to improve pronunciation. Mini real-life scenarios for practice. Personalized support to progress with confidence. 📚 Simple, clear and effective method 👨‍🏫 Support tailored to your pace

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the description: In the "Writing Commands for AI" course, we will learn how to formulate effective commands and directives that allow AI to achieve accurate and reliable results. This course is designed for anyone who wants to understand how to communicate with intelligent systems, whether you are a beginner or a technology professional. Content: Introduction to artificial intelligence and machine learning: Overview of artificial intelligence. Understand how intelligent systems work and interact with commands. Principles of writing commands: How to formulate orders clearly and specifically. Avoid vague orders and make sure directions are comprehensive. Command structure: Typical command components: inputs, processors, and outputs. Use conditional commands and repetitive operations. Test and improve commands: Methods for testing the effectiveness of commands and verifying the accuracy of the response. Optimize orders based on the results and data received. Application examples: Practical application on writing commands for various artificial intelligence systems such as chatbots, recommendation systems, and natural language processing. best practices: Tips for keeping orders organized and maintainable. How to write commands that handle unexpected situations. Expected benefits: Trainees will learn how to effectively communicate with intelligent systems to achieve desired goals more efficiently, and how to employ artificial intelligence to solve complex problems and provide continuous performance improvements. Level: Beginner Media: educational videos, practical applications, and interactive exercises.

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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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Whether you're just starting your computer science journey or preparing for university exams, I offer personalized tutoring tailored to your goals and learning style. I can also make the journey intuitive and interesting by teaching some design softwares as Canva and Adobe InDesign. I hold a Bachelor's degree in Computer Science Engineering and have experience applying technical problem-solving in international organizations alongside my work in technology, accessibility, and data analysis. I can help with: Microsoft Excel, Powerpoint and Word Canva, and Creative Designing using Adobe Programming fundamentals Python SQL and databases Data structures and algorithms Object-oriented programming Software engineering concepts Computer networks Information security fundamentals Operating systems Exam preparation Assignments and projects Problem-solving and coding practice My teaching approach focuses on building a strong understanding of concepts rather than memorization. I adapt every lesson to the student's pace, using practical examples and interactive exercises to make complex topics easier to understand. We learn together by solving creative problems, and I am available to support students at any moment of their journey. Whether you're in school, university, or simply interested in learning programming, I'll help you develop confidence and the analytical thinking skills needed to succeed.

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This course is for anyone who wants to learn to program in Python, whether you are a student, a professional, or simply curious. Python is one of the most widely used languages today, thanks to its simplicity and power. You'll learn how to write your first programs, manipulate data, automate tasks, and understand the essential foundations of modern programming. The objective is to make you independent in developing your own projects (scripts, small software, data analysis, etc.) and acquire a skill sought after in the academic and professional world.

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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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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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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.