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Engineer and senior professor of engineering sciences provides support courses in analog and digital electronics at all levels, engineering schools. having a scientific and technical knowledge, five years of experience in the field of teaching, teaching and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they have difficulties. for more info please contact me

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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 a university student, engineer, or professional who needs to actually use data — not just learn theory about it? This course is built around real problems and real code. We skip the textbook formulas and go straight to applying statistics and data science the way professionals do: with Python (pandas, NumPy, scikit-learn, matplotlib) and R (RStudio). What we cover, adapted to your level and goals: - Descriptive and inferential statistics (the ones that actually matter) - Data cleaning, exploration, and visualization - Regression, classification, and intro to machine learning - Time series and forecasting basics - R for statistical analysis and academic research Who this is for: - University students in statistics, economics, engineering, or biology - Professionals wanting to move into data analysis or data science - Researchers who need to process and present data properly I use Python and R professionally as a working engineer — everything I teach comes from real application, not just academic exercises.

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Hello, I'm doing a PhD in AI and ML using Python and am an Oracle-certified trainer with 350+ reviews and ratings [with proof attached], I will be able to teach you Python better than any of my competition. Why choose me? 1. 300 + reviews and ratings 2. Certified tutor 3. More than 5 years of teaching experience 4. Worked as a Software engineer in companies like Virtusa Corp and DIGIDEZ DIGITAL SYSTEMS 5. Hold B.tech and M.tech in Computer Science Featured Review : Been trying to learn Java on my own for about 1 year and I couldn't get a grasp on it. Aniket make learning Java a fun experience and challenges you to think for yourself to reinforce the concepts you've learned. I am truly excited for our meetings and he makes time go by so fast that I'm upset when they end. Great teacher and he is genuinely passionate about your success. If I could give him more stars I would!!! Thanks Aniket

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Do you want to learn the basics of computer science and programming, better understand your coursework, or get help with your homework and assignments? I can support you according to your level and goals! I graduated with a bachelor's degree in software engineering from Polytechnique Montréal and have been tutoring for four years. I like to take the time to explain concepts simply and adapt to each student's difficulties. I can help you with, among other things: - The basics of computer science and software development - Introduction to programming - Python, Java, C#, JavaScript and other languages as needed - Variables, conditions, loops, functions, arrays, objects, etc. - Homework, practical work and programming exercises - Understanding and reviewing concepts covered in class - Debugging and understanding errors in your code - Preparation for exams and assessments Whether you're a complete beginner or already have some knowledge, we can adapt the sessions to your level. The goal is to truly understand the concepts and gradually become more independent, rather than simply finding the answer to an exercise. The courses are suitable for high school students, college students, or anyone wishing to discover programming.

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I've been developing since I was 15, and coding has always been at the heart of my career. Initially, Java was my language of choice. Now I use Python and Nodejs. I developed a complete product which was sold to companies in order to offer Single Sign-On (SSO) to an Oracle product supporting OpenID, SAML and Kerberos. I developed in Node.js.

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Mathematics, Physics, and Computer Science Tutor | Montreal | French & English Private tutoring in mathematics, physics and chemistry, life and earth sciences, and computer science for high school, CEGEP, and university students in M French curriculum: middle school, high school, preparation for the Baccalaureate (Mathematics, Physics-Chemistry, Life and Earth Sciences) — Stanislas, Marie de France Quebec Program (Secondary & CEGEP), (NYA, NYB, NYC), university Mathematics: Secondary 1 to 5 (including SN and CST components). Science: Secondary 5 Physics and Chemistry. CEGEP: Integral and Differential Calculus (NYA, NYB), Linear Algebra (NYC), and Physics. English-language program: secondary school, CEGEP, university level Computer science: Java, C++, Linux, algorithms formations Baccalaureate with a specialization in Mathematics B.Sc. Computer Science, Finance and Mathematics — McGill M.Sc. Applied Computer Science — Concordia I have been giving private lessons in mathematics, physics-chemistry and computer science for over 10 years in Montreal. I support high school, CEGEP and university students, in Quebec, French and English programs. In mathematics and physics, I teach from secondary school to university level, including CEGEP courses at NYA, NYB, and NYC. For students at French schools in Montreal such as Stanislas or Marie de France, I cover the French curriculum from middle school through the Baccalaureate with a specialization in Mathematics, including mathematics, physics and chemistry, and life and earth sciences. In computer science, I teach programming courses in Java, C++ and Linux, as well as algorithm courses for college and university levels. My method is based on understanding before memorization. Each session is adapted to the student's level and objectives, whether it is to fill gaps in knowledge, prepare for an exam or deepen understanding of a concept. My background is rooted in both systems: I graduated with a French Baccalaureate specializing in Mathematics, hold a B.Sc. in Computer Science-Finance-Mathematics from McGill University, and an M.Sc. in Applied Computer Science from Concordia University. I have over 10 years of experience tutoring students of all levels in mathematics, physics, and computer science in Montreal.

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Developer since 10 years, I work on the development of new 3D engine for different industries. My experience ranges from raytracer to standard OpenGL rendering. The languages ​​covered will be C / C ++, OpenGL and OpenCL. This course is for anyone wanting an introduction to this type of programming or wanting to learn and discuss developments and new methods in this area. The focus is mainly on the use of graphic resources and understanding how to use the material. For beginners in programming, the course will focus on learning the technical terms, methodologies and concepts namely to program effectively in general.

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For: - Better understand your science courses (math, physics, chemistry, biology, computer science) - Find effective working methods that suit you - Regain confidence in your abilities - Discover that science can become exciting I offer personalized courses adapted to each profile which go beyond simple academic support: ✅ Learning to learn (organization, memorization, reasoning) ✅ Develop solid and sustainable methods ✅ Work at your own pace, with kindness An engineer in medical imaging, neuroscience, and artificial intelligence, my rigorous scientific background and my passion for sharing my knowledge drive me to support students in their success. My goal is to give students a taste for science and the keys to becoming independent and confident. I adapt to the pace and needs of each individual, combining rigor and kindness to restore self-confidence and rediscover the joy of learning, essential for progress.

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Having graduated with a master's degree in industrial engineering, with a major in computer science at Polytechnique Montréal, I would like to give math and/or computer science courses to students in a university program, at CEGEP or at secondary school. During my studies at Polytechnique Montréal, I gave classroom lessons, practical work (around 50 people), as well as mathematics reinforcement for all types of profiles (individual help). I also have previous private tutoring experience. It is always a real pleasure for me to witness the success of the students and to see their progress session after session. I insist on stimulating students' thinking so that they are as effective as possible during their exams. It would be a pleasure to have a first meeting!

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Hello, My name is Etienne and I am a final year student in a dual engineering school degree. I have already been a private tutor for 3 years, and I love passing on my knowledge! I am bilingual in English (985/990 on the TOEIC), and have a Master's level in Mathematics. I can also give science or computer science lessons. We can plan a face-to-face, distance or hybrid course. I hope to see you again soon!

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The aim of this course is to learn programming in general and to discover the different programming applications such as machine learning, deep learning or even video game programming via Unity. There is also the possibility of doing lessons at a more advanced level according to the student's need and to concentrate on one point or another.

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Machine Learning and Data Science are very advanced fields and, as such, in fashion. They are major tools for any new technology and the school is lagging behind in teaching these skills. In addition, these skills are theoretical as well as practical skills, and the multiple online courses focus on practice, forgetting that companies are not only looking for performers, but also experts in the intelligent use of these tools. Having advanced theoretical training in this field, along with more than 2 years of field experience in information programming associated with machine learning, I propose to teach you this subject, both theory and practice, at the option of courses combining the two aspects of the thing.

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Hello, I am a trainee engineer at MBA and I have 19 years of experience in the field. I teach web and mobile programming courses (Spring, Java, Hibernate, Angular, HTML5, CSS3, etc.) I am available on Saturdays. thank you,

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Homework help. Exam preparation. Preparing for job interviews. You can attend the course alone or with a group of 5 people maximum.

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This course aims to provide a solid foundation of programming concepts and techniques, using popular programming languages such as C, C++ or Python. This course is suitable for people who are new to programming or who already have some basic knowledge and want to improve their skills. Whether you want to become a software developer, get into data analysis, or simply learn to code, this course will provide you with the essential knowledge and practical skills needed to succeed.

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Hello, Graduated in 2016 as a computer engineer and application developer for a computer company, I have acquired strong programming skills over the last 5 years. Today, I give tutoring all levels on the following technologies: JAVA / PHP / HTML / CSS / SQL If you need help to: - Realize a TP / Project - Review specific points on a chapter - Prepare a control - Or simply learn programming So I can help you! My method is simple: each of my classes is followed by exercises to put into practice what you have just learned. I use a lot of schemas because they are easier to understand than a long explanatory text. For anyone outside Montreal, know that I also give lessons via skype through screen sharing, and that the course is as instructor as a face to face. You are interested ? send me a message on Apprentices!

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With a doctorate in robotics and an associate professor of Industrial Engineering Sciences, I offer science courses tailored to each student, for secondary, college and university levels (1st and 2nd cycle). With several years of experience in teaching and individual coaching, I can help you to: - strengthen your foundations in mathematics, physics, and engineering sciences, - develop your work methodology, - to prepare effectively for your exams and competitions.

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I have 10 years of experience in Computer Science and I am a mathematician. I can help you solve your problems. Courses, Projects and exercises. I help you solve problems and learn the methodology of the work. I can help you solve your problems.

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22Students

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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Learn Python by building something you’ll actually use Ever wanted to code but had no idea where to begin? You’re in the right place. In these relaxed, one-on-one lessons, you’ll go from zero to writing real programs, with no experience required. Together, we’ll choose a project that fits your interests. Maybe it’s a video game you want to build or a quiz to challenge your friends, a chart built from your own spreadsheet, or a script that takes over a boring, repetitive task. Along the way, you’ll pick up the essentials (variables, loops, functions) and learn to read error messages without panic, so you can change your code with confidence. Where you can go next AI-assisted coding: Use tools like ChatGPT to brainstorm, decode errors, and improve your code, while learning to test its suggestions and explain how the final result works. Machine learning: Train a simple model on a real dataset and see how well it predicts. Your own project: Bring an idea from school, work, or a hobby, and we’ll break it into steps you can build at your own pace. Your instructor I’m an engineer with a master’s degree from Nagoya University and research experience in applied AI and computer vision. I explain every concept in plain language, then give you time to try it yourself with live feedback, so you’re never stuck alone. Ready to start? Send me a quick message and let's do your first coding project together.

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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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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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These courses are part of a structured and progressive training in Object-Oriented Programming (OOP) with JavaScript, designed for beginner or intermediate developers who want to understand in depth how the language works, write clearer, more maintainable code and prepare themselves calmly for modern frameworks like React ⚛️. Object-Oriented Programming is often perceived as complex or abstract. My goal is simple: to make it logical, concrete, and immediately applicable. 🎯 Training Objectives Upon completion of this training, you will be able to: Understanding what Object-Oriented Programming really is (and when to use it) Create and manipulate objects in JavaScript in a clean and efficient way Use ES6 classes, constructors, and methods with confidence Mastering this, the prototype, and the instantiation logic Apply encapsulation, inheritance, and polymorphism without confusion Avoiding common mistakes made by OOP beginners Structure your JavaScript code like a professional developer 📖 Training Plan – Object-Oriented Programming in JavaScript 1. Introduction to Object-Oriented Programming 🧠 Understanding the concept, objectives and benefits of OOP. 2. Procedural Programming vs. OOP Why unstructured code quickly becomes unmanageable. 3. Objects in JavaScript Properties, methods and representation of the real world. 4. The keyword this Understanding the execution context (often poorly understood). 5. Limitations of simple objects Why duplicating code is a bad idea. 6. Constructive functions Create multiple objects from the same model. 7. The keyword new What it's actually doing under the hood. 8. The prototype Sharing methods and memory optimization. 9. ES6 Classes Modern syntax and best practices. 10. The builder Proper initialization of objects. 11. Data Encapsulation Protect the internal state of objects. 12. Inheritance between classes Reusing code intelligently. 13. The keyword super Communication between parent and child in the classroom. 14. Polymorphism The same behavior, several forms. 15. Composition vs. Inheritance Choosing the right architecture. 16. Best practices in OOP Write readable, scalable, and maintainable code. 17. Common mistakes made by beginners Pitfalls to absolutely avoid. 18. Guided practical exercise Creation of a concrete class (product, user, etc.). 19. Assessment Quiz (Multiple Choice Questions) To validate the actual understanding of the concepts. 🛠️ Teaching method: Understand before writing This training program is based on a progressive and pragmatic approach: Clear and illustrated explanations Concrete examples from real projects Simple but effective exercises Constant questioning to avoid rote learning Adaptation to the learner's level and pace Here, we don't "recite OOP" — we understand it. 🚀 Learner's result At the end of the training, you will not only know how to write a JavaScript class. You will know: 1- Why does it exist? 2- When to use it 3- and when not to use it You will leave with: a solid understanding of OOP a cleaner and more professional code an ideal foundation for learning React, Node.js or any other modern framework