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Since March 2021
Instructor since March 2021
Translated by GoogleSee original
Computer courses (development in Python, C, C++, C#, etc.)
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From 76 C$ /h
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Presentation :

Data Science and AI engineer.
IT consultant for 3 years in a Champagne House.

Methodology :

- online course
- possibility of sending training exercises (with correction, or correction directly during the next lesson)
- I am available every day for help with an exercise that poses a problem or other
- possibility of resuming your lessons and doing help sessions for your exercises, DM

Course:

Engineering degree in Artificial Intelligence and Data Science at EPITA
I also hold a Bac S, math option I did two years of preparatory Maths and Physics with Computer Science option (MPSI and MP).
Location
location type icon
Online from France
About Me
Diplômé de l'EPITA ( l'école des ingénieurs en intelligence informatique ) en août 2022, je suis actuellement consultant IT chez Champagne Perle Blanche.
Avant cela j'ai passé 2 ans en classe préparatoire MPSI/MP spécialisée en maths et physique.

Je suis maintenant spécialisé en Data science et intelligence artificielle.

Je donne des cours depuis plus d'un an maintenant.
Education
- Diplôme d'ingénieur en intelligence artificielle et en science des données à l'EPITA (L'ÉCOLE DES INGÉNIEURS EN INTELLIGENCE INFORMATIQUE).

- Classe préparatoire MPSI/MP

- Bac S
Experience / Qualifications
Expérience cours : Plus d'un centaine d'heure de cours déjà donné en Maths et Informatique.
Expérience professionnelles : data Scientist chez Roche ( 6 mois ) et IT consultant chez Champagne Perle Blanche ( depuis 3 ans )
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
30 minutes
45 minutes
60 minutes
90 minutes
120 minutes
The class is taught in
English
French
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
Presentation :

Data Science and AI engineer.
IT consultant for 3 years in a Champagne House.

Methodology :

- online course
- possibility of sending training exercises (with correction, or correction directly during the next lesson)
- I am available every day for help with an exercise that poses a problem or other
- possibility of resuming your lessons and doing help sessions for your exercises, DM

Course:

Engineering degree in Artificial Intelligence and Data Science at EPITA
I also hold a Bac S, math option I did two years of preparatory Maths and Physics with Computer Science option (MPSI and MP).
Read more
Presentation :

Data Science and AI engineer.
IT consultant for 3 years in a Champagne House.

Methodology :

- online course
- possibility of sending training exercises (with correction, or correction directly during the next lesson)
- I am available every day for help with an exercise that poses a problem or other
- possibility of resuming your lessons and doing help sessions for your exercises, DM

Course:

Engineering degree in Artificial Intelligence and Data Science at EPITA
I also hold a Bac S, math option I did two years of preparatory Maths and Physics with Computer Science option (MPSI and MP).
Read more
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• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
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• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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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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Learn to code with method and logic
Whether it's to succeed in the NSI (Digital Sciences and Technology) specialization in high school, design personal projects, or prepare for higher scientific studies, mastering code relies on solid algorithmic thinking. I help students understand the structure of programming languages and the logic of data.

Subject areas and languages taught:

Algorithms & Logic: Designing data structures and solving problems.

Programming Languages: Python, C/C++, C# and Java.

Data Management: Analysis and SQL queries / databases.

Basic Web Development: HTML & CSS for creating structured pages.

The goal is to take the student from simply writing code to true autonomy in development.
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Python fundamentals: variables, loops, functions, data structures
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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.
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verified badge
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Whether you are just starting to learn Python or already have a specific project and need some guidance, I would be happy to help you.

My goal is to explain things clearly, adapt to your level, and help you understand not only how to make something work, but also why it works.

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Python fundamentals through a finance lens (data structures, functions, control flow).
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• Lecturer at the American University AUC
• Over 20 years of experience in training students for government employees, oil companies (BP), food companies (Nestle), banks (CIB), and telecommunications companies (Vodafone).

• Teaching curricula, syllabuses, courses:
o IGCSE (Computer Science 0478, ICT 0417)
o Programming and computer courses for all educational levels (from primary to university)
o Microsoft Windows, Word, Excel, PowerPoint, Outlook, MS-Project
o Programming, C, C++, VB.NET, C#, Python, Database, SQL, MQL, VBA
o HTML, CSS, JavaScript, Angular
o Different database systems
o Data analysis using Excel
o Computer and Information Colleges Curricula
o Using artificial intelligence in life and work

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• Prepare yourself to work as a Front-End / Back-End / Full Stack Developer
• Theoretical and practical training for market requirements
• Don't miss out on technology. Lessons are designed for the elderly, in a simple and understandable way (use of computers and their programs, use of mobile phones, dealing with the Internet and social media).
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I teach Python, C and C++ one to one, online or in person around Birmingham.

Most of my students fall into one of three groups. Some are at GCSE or A-Level and need to get comfortable with a language before an exam or a coursework deadline. Some are at university, usually on an engineering or computing degree, and have hit something specific that isn't clicking: pointers, memory, recursion, object orientation, or a project that won't compile. And some are adults starting from nothing, often because work has started asking them to automate things.

Lessons are built around code you can run. I'll ask what you're working on and where you got stuck, then we write something small together, break it on purpose, and work out what the error message is actually telling you. Reading error messages properly is half of programming and almost nobody teaches it.

Areas I cover regularly:

Python from the basics through functions, data structures, file handling, object orientation and libraries like NumPy and Pandas
C and C++, including the parts that cause most of the trouble: pointers, memory management, structs, classes and compilation
GCSE and A-Level Computer Science across all exam boards, including pseudocode, trace tables and written paper technique
A-Level NEA projects and university coursework, plus debugging sessions and code review
Embedded C for Arduino, ESP32 and microcontroller projects, which is the work I do professionally

After each lesson I send written notes covering what we did, worked through step by step, so you have something to revise from later rather than trying to remember what was on screen.

First session is free and lasts 30 minutes. We use it to work out what you need and whether I'm the right person for it. If I'm not, I'll say so and point you somewhere better.

Message me with what you're studying and what's giving you trouble, and I'll tell you honestly how I'd approach it.
verified badge
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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Learn Python with a structured, hands-on approach! Whether you are a absolute beginner looking to start coding, a student needing help with coursework, or a professional aiming to automate tasks, these lessons are tailored for you.What we will cover:
Core Fundamentals: Variables, loops, functions, and data structures.Object-Oriented Programming: Building reusable and clean code.Real-World Projects: Creating scripts, data analysis, or web scraping based on your goals.

Problem Solving: Learning how to debug and think like a programmer. Lessons are highly interactive. We will write code together from day one, and you will receive practical exercises after every session to build your confidence.
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Master Computer Science & Coding Concepts Easily!

Computer Science doesn't have to be complicated. I focus on simplifying complex logic, algorithms, and practical programming so you can build strong foundational knowledge.

What You Will Learn:

Python Fundamentals: Data types, loops, logic, and object-oriented programming (OOP).

Data Structures & Algorithms: Practical logic building and problem-solving techniques.

Database & SQL: Basics of designing relational databases and writing queries.

Data Analysis Tools: Introduction to Python libraries like NumPy and Pandas for real-world applications.

Teaching Approach:

Interactive live sessions with code-along exercises.

Step-by-step breakdown of academic homework and practical assignments.

Patient, structured, and student-centric support.

Feel free to send a message or book a lesson to get started on your tech journey!
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I am an experienced computer science teacher with many years of teaching experience and a university degree in Mathematics and Computer Science.

I offer individual online lessons in programming and computer science for school students, as well as support for university students in selected subjects. Lessons can cover Python, MATLAB, SQL and databases, algorithms and programming fundamentals, computer systems, and web development with HTML, CSS and JavaScript.

My lessons are adapted to each student's previous knowledge, current curriculum and individual goals. I explain concepts step by step and focus on understanding the logic behind programming rather than simply memorizing code.

We can work on current school or university topics, programming exercises and assignments, fill gaps in knowledge, prepare for tests and exams, or develop practical programming skills.

Lessons are taught online in Serbian, Bosnian or Croatian, which can be particularly helpful for students from families from the former Yugoslavia who live and study in Germany, Austria, Switzerland or other countries.
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Learn to code by creating your own games and interactive projects! These online lessons are designed for children and teenagers aged 7–17, from complete beginners to students with some coding experience.

We choose Scratch, Python, or Roblox Studio based on your child’s age, interests, and level. Students learn programming concepts, practise logical thinking, and discover how to find and fix errors independently.

I’ve been teaching since 2018 and have five years of software development experience. Each lesson combines clear explanations with practical activities in a friendly environment where questions are always welcome.

Students also get access to my learning platform to review materials and practise between lessons.
Good-fit Instructor Guarantee
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