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Since July 2017
Instructor since July 2017
Docker for developers from beginner to guru. A must have skill that any developer should master!
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From 96 C$ /h
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Docker is an essential tool for any developer nowadays. It allows flexibility and speeds up development.
Whether you would like to start learning containerization or you would like to take your knowledge to the next level, I can help you. Docker is a great tool to not only deliver software but also to play around with different technologies and test multiple releases without burdening your local environment.
Extra information
Best if you are using Linux or MacOS. If using Windows, make sure you have Docker installed according to the official documentation.
Location
location type icon
Online from Netherlands
About Me
I am involved with computers and software development both at work and in my private life since more than 20 years. I am passionate about tutoring and sharing my knowledge and experience. I worked with students of various ages teaching them language, math, Office applications and software development.
I have solid knowledge of Golang, Javascript, Java, Docker, Kubernetes, Linux, Bash, Git, Gitlab, Jenkins.
Education
I studied extensively mathematics and physics in high school. I have a Bachelor in Computer Science from the Polytechnic University of Bucharest. I learned English at school and Spanish, German and Italian on my own.
Experience / Qualifications
Software developer and team leader. I am Docker Certified Associate and lead a devops team. I use Golang, Java and Javascript to write software for me and my clients.
I worked in many companies and with various cultures. As a tutor I always managed to help my students pass their exams with excellence as well as their fears of computers.
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
English
Spanish
Romanian
German
Reviews
Availability of a typical week
(GMT -04:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
Go (Golang) is a great option either as your first programming language or as a next step in your already established developer carrier.
Some of its concepts and idioms might seems difficult to grasp but with the right guidance you will discover that they make sense and they make development really fun and easy.
I am happy to share with you my passion for Go so just send me a message and let's start!
Read more
Either you want to understand how computers work in general, what are the risks of online activities or you want to start or improve your developer skills, I will be happy to help you with that.
I am specialized in Java, GOlang, Docker, Git, HTML, CSS, JS, jQuery and I work in the field for more than 20 years.
I have experience in coaching professionals as well as in introducing children and teenagers to the fascinating world of computers and especially programming.
Read more
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Hello, I am a doctoral student in electrical engineering and associate professor in engineering sciences, experienced in the field of electrical engineering, I offer support courses in the subjects of engineering sciences (Electronics, automatics, electrical engineering, automation, programming).

Digital electronics
Analog electronic
electromagnetism (propagation of high frequency waves)
Automatic (continuous, sampled)
electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
renewable energy (wind, PV)
engineering Sciences
RDM
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These courses allow the student to get up to speed and regain confidence in all scientific subjects, just as they prepare him effectively for the Baccalaureate, the Preparatory Classes or various examinations of the engineering classes.

COURSE OBJECTIVES AND PEDAGOGICAL APPROACH

Resumption and deepening of fundamental concepts through exercises with course reminders.

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This educational approach is effective since it has often led me to interesting results with my students.

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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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doctoral student in engineering sciences provides support courses in analog and digital electronics at any DEUG level and engineering schools. having scientific and technical knowledge, three years of experience in the field of teaching, pedagogy and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they are having difficulty. for more info please contact me
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Don't settle for anything less than excellence.
I am an Aerospace Engineer with a Master's degree in Quantum Physics and have completed Ph.D. work at the University of Cambridge in Computational Physics. Additionally, I have 4 years of experience developing MATLAB and possess deep programming skills in MATLAB/Simulink family, C/C++, Fortran, and Python.

With over 12 years of tutoring experience, I have successfully guided more than 50 students worldwide to achieve distinction in various fields. Consistent results are my priority, and I strive for excellence in all aspects of my teaching.

My lessons are customized to meet each student's unique needs and are designed to be engaging and insightful.

Whether you are at a school level or require advanced or professional-level instruction, I offer support in the following areas:
- Preparation for IB/IA, A-Levels, GCSE, University Entry, or equivalent.
- Experience in preparing students to access world-class schools and universities, including Cambridge University, Oxford, Ivy League and other top institutions in the UK and US.
- University levels (undergraduate and postgraduate).
- High school studies and diploma programs.
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- Extensive experience working with children.

Every lesson is meticulously planned in advance to ensure that it aligns with your goals and targets areas for improvement.
I prioritize a dynamic and interactive learning experience, with one-on-one sessions tailored to your individual requirements. Lessons will be conducted via webcam, enabling you to connect from anywhere.

I have a highly flexible schedule and can adapt to accommodate your needs.
If you have any questions about my teaching method, availability, or pricing, please don't hesitate to reach out. I am here to assist you and provide the support you need.
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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)
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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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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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This preparation session is dedicated to students aiming for preparatory classes for scientific Grandes Ecoles (CPGE), with a particular focus on the subjects of Physics and Engineering Sciences. The goal is to strengthen the foundations and deepen the knowledge to succeed.

1. Mechanics:
Kinematics: Study of rectilinear and circular movements, position vectors, speed and acceleration.
Dynamics: Newton's laws, work and energy, kinetic energy theorem.

2. Electromagnetism / Electrokinetics:
Electrostatics: Electric charges and fields, electric potential, capacitance.
Magnetostatics: Magnetic fields, Lorentz forces, electromagnetic induction.
Alternating Currents: RLC circuits, resonance, impedance.

3. Thermodynamics:
Principles of thermodynamics: Internal energy, heat, work, first and second principles.
Ideal and real gases: Equations of state, thermodynamic transformations.

4. Industrial sciences:
Automatic Linear, Kinematic, Static.

For more information and to register for the preparation session, please contact me.

Good preparation and success in your studies!

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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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With over seven years of experience in teaching Computer Science & Information Technology (ICT), I have developed a strong expertise in delivering high-quality education across multiple internationally recognized curricula, including Cambridge IGCSE, GCSE, A-Levels, O-Levels, and Checkpoint. My passion lies in equipping students with coding, cybersecurity, and digital literacy skills, ensuring they are well-prepared for the evolving demands of the digital world.

Expertise & Teaching Areas:
✅ Programming & Software Development: Python, Java, C++
✅ Cybersecurity: Ethical hacking, data protection, network security
✅ Digital Literacy: ICT applications, online safety, cloud computing
✅ Data Science & AI: Data analysis, machine learning fundamentals
✅ Web Development: HTML, CSS, JavaScript

Curriculum & Pedagogical Experience:
🔹 Cambridge IGCSE & GCSE ICT & Computer Science – Teaching core and extended syllabi, focusing on programming logic, databases, and networking.
🔹 Cambridge A-Levels & O-Levels Computer Science – Preparing students for advanced computing concepts, problem-solving, and algorithm development.
🔹 Cambridge Checkpoint ICT – Building foundational skills in digital technology and computer applications.

Professional Impact:
📌 Mentored students to achieve top grades in Cambridge ICT & Computer Science exams.
📌 Developed interactive lesson plans integrating real-world applications of technology.
📌 Conducted coding boot camps and cybersecurity workshops to enhance practical learning.
📌 Guided students in project-based learning, including app development and website design.

With a strong commitment to student-centered learning and technological innovation, I am dedicated to shaping future tech leaders and empowering learners with skills relevant to careers in technology, data science, and software development.
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This course introduces students to the fundamentals of Information and Communication Technology (ICT) and its role in modern society. Topics include computer hardware and software, digital communication tools, internet technologies, data management, cybersecurity, and emerging trends. Students will gain practical skills in using productivity software, conducting online research, and understanding the ethical and responsible use of digital resources. The course emphasizes both technical proficiency and digital literacy, preparing learners to confidently navigate and contribute to a technology-driven world.
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# **Master C/C++: Build the Foundation of Modern Software Development**

Unlock the power of one of the most influential programming languages in computing history! Whether you're an absolute beginner or looking to deepen your expertise, this comprehensive C/C++ course delivers structured learning from fundamentals to advanced concepts that power operating systems, game engines, and high-performance applications.

## **Why Choose This C/C++ Program?**

**Industry-Relevant Curriculum:** Learn expert guidance on the design of effective classes, functions, templates, and inheritance patterns that form the backbone of professional C++ development. Move beyond basic syntax to understand how to write clean, efficient, and maintainable code that stands the test of time.

**Templates & Generic Programming Mastery:** Go beyond introductory material with in-depth coverage of templates—the cornerstone of modern C++—enabling you to create robust, reusable code components that work across multiple data types. Discover how function templates, class templates, and variadic templates work to maximize your coding efficiency.

**Practical, Hands-On Approach:** This isn't just theory! You'll build real-world projects that demonstrate memory management, object-oriented programming, and system-level programming techniques used in today's technology landscape.

## **Your Learning Journey**

Our structured path takes you from writing your first "Hello World" program through advanced template metaprogramming, with special attention to modern C++ standards (up to C++20). You'll gain the confidence to tackle complex programming challenges and understand the "why" behind effective C++ practices—not just the "how."

## **Transform Your Career Today**

C/C++ skills remain in high demand across industries from finance to gaming to IoT. By mastering these foundational languages, you'll develop problem-solving abilities that translate to any programming environment.
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PhD Candidate in Informatics – Private Lessons & Pancyprian Exams

I am a PhD Candidate in Informatics and I offer private lessons in Informatics to High School students (Pancyprian Exams) as well as to University students, with an emphasis on correct understanding and methodical thinking.

Pancyprian Exams – Informatics

Systematic preparation with an emphasis on:
• understanding of the material
• correct algorithmic thinking
• methodology for solving problems
• analysis of old Pancyprian exam questions

We cover, for example: pseudocode, tables, repetitions, control structures and common exam errors.

Students & General Computing

Support in:
• Programming (C / C++ / Python)
• Operating Systems
• Computer Architecture
• Code Understanding & Debugging

In-person or online courses, with emphasis on understanding and proper study organization.

English text below

PhD Candidate in Computer Science – Private Tutoring & Pancyprian Exams

I am a PhD candidate in Computer Science offering private tutoring for high school students (Pancyprian Exams – Computer Science) and university students.

Pancyprian Exams – Computer Science

Structured exam preparation focusing on:
• understanding the syllabus
• correct algorithmic thinking
• exam-oriented problem-solving
• analysis of past Pancyprian exams

Topics include pseudocode, arrays, loops, control structures, and common exam mistakes.

University & General Computer Science

Support in:
Programming (C/C++/Python)
• Operating Systems
• Computer Architecture
• Code understanding and debugging

Lessons are available in person or online, with emphasis on understanding concepts rather than memorization.
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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
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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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Professeur agrégé de informatique, j’aide élèves et étudiants à réussir examens et concours. J’interviens aux classes préparatoires (MPSI, MP, PSI, ECS...) et jusqu’à l’université (Licence & Master en sciences ou économie). Ma méthode : comprendre le cours, pratiquer avec rigueur, structurer le raisonnement et ha des exercices et problèmes bien choisis. Chaque séance inclut exercices ciblés, conseils méthodologiques, et suivi personnalisé. Vous recevez un enregistrement vidéo plus un PDF annoté après chaque cours. Cours en ligne via Google Meet, 5 jours sur 7, avec flexibilité horaire. Je reste joignable entre les séances pour répondre aux questions. Contactez-moi pour un premier échange.
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Hello, I am a doctoral student in electrical engineering and associate professor in engineering sciences, experienced in the field of electrical engineering, I offer support courses in the subjects of engineering sciences (Electronics, automatics, electrical engineering, automation, programming).

Digital electronics
Analog electronic
electromagnetism (propagation of high frequency waves)
Automatic (continuous, sampled)
electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
renewable energy (wind, PV)
engineering Sciences
RDM
Python,VHDL
PIC Microprocessor and Microcontroller
Signal processing and data acquisition
Engineering Sciences

These courses allow the student to get up to speed and regain confidence in all scientific subjects, just as they prepare him effectively for the Baccalaureate, the Preparatory Classes or various examinations of the engineering classes.

COURSE OBJECTIVES AND PEDAGOGICAL APPROACH

Resumption and deepening of fundamental concepts through exercises with course reminders.

Put the student in a situation of questioning and research.

Respond to individual issues and questions

Exercise training in order to achieve real mastery of the content.

Learn to build theoretical reasoning from observable facts or hypotheses.

Specific preparation for higher education requirements (in-depth content, increase in work capacity, enrichment of scientific background)

This educational approach is effective since it has often led me to interesting results with my students.

Associate professor provides support courses in electrical engineering
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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.
verified badge
doctoral student in engineering sciences provides support courses in analog and digital electronics at any DEUG level and engineering schools. having scientific and technical knowledge, three years of experience in the field of teaching, pedagogy and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they are having difficulty. for more info please contact me
verified badge
Don't settle for anything less than excellence.
I am an Aerospace Engineer with a Master's degree in Quantum Physics and have completed Ph.D. work at the University of Cambridge in Computational Physics. Additionally, I have 4 years of experience developing MATLAB and possess deep programming skills in MATLAB/Simulink family, C/C++, Fortran, and Python.

With over 12 years of tutoring experience, I have successfully guided more than 50 students worldwide to achieve distinction in various fields. Consistent results are my priority, and I strive for excellence in all aspects of my teaching.

My lessons are customized to meet each student's unique needs and are designed to be engaging and insightful.

Whether you are at a school level or require advanced or professional-level instruction, I offer support in the following areas:
- Preparation for IB/IA, A-Levels, GCSE, University Entry, or equivalent.
- Experience in preparing students to access world-class schools and universities, including Cambridge University, Oxford, Ivy League and other top institutions in the UK and US.
- University levels (undergraduate and postgraduate).
- High school studies and diploma programs.
- Assistance with specific projects at a professional level, including job interview preparation.
- Extensive experience working with children.

Every lesson is meticulously planned in advance to ensure that it aligns with your goals and targets areas for improvement.
I prioritize a dynamic and interactive learning experience, with one-on-one sessions tailored to your individual requirements. Lessons will be conducted via webcam, enabling you to connect from anywhere.

I have a highly flexible schedule and can adapt to accommodate your needs.
If you have any questions about my teaching method, availability, or pricing, please don't hesitate to reach out. I am here to assist you and provide the support you need.
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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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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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This preparation session is dedicated to students aiming for preparatory classes for scientific Grandes Ecoles (CPGE), with a particular focus on the subjects of Physics and Engineering Sciences. The goal is to strengthen the foundations and deepen the knowledge to succeed.

1. Mechanics:
Kinematics: Study of rectilinear and circular movements, position vectors, speed and acceleration.
Dynamics: Newton's laws, work and energy, kinetic energy theorem.

2. Electromagnetism / Electrokinetics:
Electrostatics: Electric charges and fields, electric potential, capacitance.
Magnetostatics: Magnetic fields, Lorentz forces, electromagnetic induction.
Alternating Currents: RLC circuits, resonance, impedance.

3. Thermodynamics:
Principles of thermodynamics: Internal energy, heat, work, first and second principles.
Ideal and real gases: Equations of state, thermodynamic transformations.

4. Industrial sciences:
Automatic Linear, Kinematic, Static.

For more information and to register for the preparation session, please contact me.

Good preparation and success in your studies!

.
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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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With over seven years of experience in teaching Computer Science & Information Technology (ICT), I have developed a strong expertise in delivering high-quality education across multiple internationally recognized curricula, including Cambridge IGCSE, GCSE, A-Levels, O-Levels, and Checkpoint. My passion lies in equipping students with coding, cybersecurity, and digital literacy skills, ensuring they are well-prepared for the evolving demands of the digital world.

Expertise & Teaching Areas:
✅ Programming & Software Development: Python, Java, C++
✅ Cybersecurity: Ethical hacking, data protection, network security
✅ Digital Literacy: ICT applications, online safety, cloud computing
✅ Data Science & AI: Data analysis, machine learning fundamentals
✅ Web Development: HTML, CSS, JavaScript

Curriculum & Pedagogical Experience:
🔹 Cambridge IGCSE & GCSE ICT & Computer Science – Teaching core and extended syllabi, focusing on programming logic, databases, and networking.
🔹 Cambridge A-Levels & O-Levels Computer Science – Preparing students for advanced computing concepts, problem-solving, and algorithm development.
🔹 Cambridge Checkpoint ICT – Building foundational skills in digital technology and computer applications.

Professional Impact:
📌 Mentored students to achieve top grades in Cambridge ICT & Computer Science exams.
📌 Developed interactive lesson plans integrating real-world applications of technology.
📌 Conducted coding boot camps and cybersecurity workshops to enhance practical learning.
📌 Guided students in project-based learning, including app development and website design.

With a strong commitment to student-centered learning and technological innovation, I am dedicated to shaping future tech leaders and empowering learners with skills relevant to careers in technology, data science, and software development.
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This course introduces students to the fundamentals of Information and Communication Technology (ICT) and its role in modern society. Topics include computer hardware and software, digital communication tools, internet technologies, data management, cybersecurity, and emerging trends. Students will gain practical skills in using productivity software, conducting online research, and understanding the ethical and responsible use of digital resources. The course emphasizes both technical proficiency and digital literacy, preparing learners to confidently navigate and contribute to a technology-driven world.
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# **Master C/C++: Build the Foundation of Modern Software Development**

Unlock the power of one of the most influential programming languages in computing history! Whether you're an absolute beginner or looking to deepen your expertise, this comprehensive C/C++ course delivers structured learning from fundamentals to advanced concepts that power operating systems, game engines, and high-performance applications.

## **Why Choose This C/C++ Program?**

**Industry-Relevant Curriculum:** Learn expert guidance on the design of effective classes, functions, templates, and inheritance patterns that form the backbone of professional C++ development. Move beyond basic syntax to understand how to write clean, efficient, and maintainable code that stands the test of time.

**Templates & Generic Programming Mastery:** Go beyond introductory material with in-depth coverage of templates—the cornerstone of modern C++—enabling you to create robust, reusable code components that work across multiple data types. Discover how function templates, class templates, and variadic templates work to maximize your coding efficiency.

**Practical, Hands-On Approach:** This isn't just theory! You'll build real-world projects that demonstrate memory management, object-oriented programming, and system-level programming techniques used in today's technology landscape.

## **Your Learning Journey**

Our structured path takes you from writing your first "Hello World" program through advanced template metaprogramming, with special attention to modern C++ standards (up to C++20). You'll gain the confidence to tackle complex programming challenges and understand the "why" behind effective C++ practices—not just the "how."

## **Transform Your Career Today**

C/C++ skills remain in high demand across industries from finance to gaming to IoT. By mastering these foundational languages, you'll develop problem-solving abilities that translate to any programming environment.
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PhD Candidate in Informatics – Private Lessons & Pancyprian Exams

I am a PhD Candidate in Informatics and I offer private lessons in Informatics to High School students (Pancyprian Exams) as well as to University students, with an emphasis on correct understanding and methodical thinking.

Pancyprian Exams – Informatics

Systematic preparation with an emphasis on:
• understanding of the material
• correct algorithmic thinking
• methodology for solving problems
• analysis of old Pancyprian exam questions

We cover, for example: pseudocode, tables, repetitions, control structures and common exam errors.

Students & General Computing

Support in:
• Programming (C / C++ / Python)
• Operating Systems
• Computer Architecture
• Code Understanding & Debugging

In-person or online courses, with emphasis on understanding and proper study organization.

English text below

PhD Candidate in Computer Science – Private Tutoring & Pancyprian Exams

I am a PhD candidate in Computer Science offering private tutoring for high school students (Pancyprian Exams – Computer Science) and university students.

Pancyprian Exams – Computer Science

Structured exam preparation focusing on:
• understanding the syllabus
• correct algorithmic thinking
• exam-oriented problem-solving
• analysis of past Pancyprian exams

Topics include pseudocode, arrays, loops, control structures, and common exam mistakes.

University & General Computer Science

Support in:
Programming (C/C++/Python)
• Operating Systems
• Computer Architecture
• Code understanding and debugging

Lessons are available in person or online, with emphasis on understanding concepts rather than memorization.
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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
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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

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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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Professeur agrégé de informatique, j’aide élèves et étudiants à réussir examens et concours. J’interviens aux classes préparatoires (MPSI, MP, PSI, ECS...) et jusqu’à l’université (Licence & Master en sciences ou économie). Ma méthode : comprendre le cours, pratiquer avec rigueur, structurer le raisonnement et ha des exercices et problèmes bien choisis. Chaque séance inclut exercices ciblés, conseils méthodologiques, et suivi personnalisé. Vous recevez un enregistrement vidéo plus un PDF annoté après chaque cours. Cours en ligne via Google Meet, 5 jours sur 7, avec flexibilité horaire. Je reste joignable entre les séances pour répondre aux questions. Contactez-moi pour un premier échange.
Good-fit Instructor Guarantee
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