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Since October 2026
Instructor since October 2026
Learn Python by Building Games & Small Projects | Kids, Teens & Beginners
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From 26 Fr /h
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Learn to code by making something you can run, change and understand. These one-to-one online Python lessons are designed for children, teens and adult beginners who want a clear, practical introduction to programming. No previous coding experience is needed.

We can begin with a simple quiz, number-guessing game or useful everyday program. Along the way, you will learn variables, input and output, decisions, loops, functions and debugging. Each project is adapted to your age, interests and pace. We discuss why the code works, practise making changes and gradually build independence.

I’m Aymen, an online tutor based in Kingston, Ontario, Canada, with eight years of online teaching experience. I completed the requirements for a Master of Applied Science in Electrical & Computer Engineering at Queen’s University in September 2025, with an AI focus. My background includes teaching assistant roles at Queen’s University and LUMS, and technical skills in Python, machine learning, PyTorch, speech AI, RAG, FastAPI and Docker.

Lessons are available in English. I guide learners through their own code and help them understand and solve problems rather than completing their work for them. Share the learner’s age, interests and any previous coding experience before booking.
Extra information
Please use a laptop or desktop with a reliable internet connection and microphone. Beginners can use a browser-based Python environment initially; we can discuss a suitable setup before class. Younger learners may need a parent’s help joining the first session.
Location
location type icon
Online from Canada
About Me
Hi, I’m Aymen, an online math, Python and AI tutor based in Kingston, Ontario, Canada. I completed the requirements for a Master of Applied Science in Electrical & Computer Engineering at Queen’s University in September 2025, with an AI focus. I have eight years of online teaching experience and have held teaching assistant roles at Queen’s University and LUMS.

I help children, teens and adults learn through clear explanations, guided practice and practical projects. I teach in English and use a digital pen or drawing tablet to make each step easy to follow.

Math lessons can cover elementary foundations, algebra, geometry, pre-calculus and calculus. Coding lessons can cover Python fundamentals, debugging and practical projects, while AI lessons can explore machine learning and tools suited to the learner’s level. All lessons are delivered online and adapted to your goals and pace.
Education
Master of Applied Science in Electrical & Computer Engineering, Queen’s University, Kingston, Ontario. Studied September 2023–September 2025; completed degree requirements in September 2025, with an AI focus.
Experience / Qualifications
Eight years of online teaching experience. Teaching assistant roles at Queen’s University and LUMS, with experience supporting programming, data science and AI learning. Online lessons use guided practice, clear explanations and practical projects.
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Duration
60 minutes
The class is taught in
English
Skills
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
Build confidence in math, coding and AI with lessons tailored to your age, level and goals. I teach children, teens and adult learners online in English.

Math lessons can cover elementary foundations, algebra, geometry, pre-calculus and calculus. We break challenging ideas into manageable steps, work through examples together and build understanding through guided practice.

Python lessons can begin with simple games and practical projects for children and beginners, or focus on programming concepts, debugging and more advanced coding. AI lessons can cover machine learning and practical AI tools. My technical background includes Python, PyTorch, speech AI, retrieval-augmented generation (RAG), FastAPI and Docker.

I completed the requirements for a Master of Applied Science in Electrical & Computer Engineering at Queen’s University in September 2025, with an AI focus. I have eight years of online teaching experience, including teaching assistant roles at Queen’s University and LUMS.

I use clear explanations, a digital pen or drawing tablet, and activities suited to the learner. I help students understand and complete their own work. All lessons are delivered online from Canada.
Read more
Does your child understand math in class but get stuck when working alone? These one-to-one online lessons help children and teens build stronger foundations, understand each step and become more confident solving problems independently.

Lessons can cover elementary arithmetic, fractions, decimals, percentages, pre-algebra, algebra, geometry, pre-calculus and calculus. We start with the learner’s current level and school topics, identify the gaps, then work through clear explanations, examples and guided practice. I use a digital pen or drawing tablet so students can follow the working visually.

I’m Aymen, based in Kingston, Ontario, Canada. I completed the requirements for a Master of Applied Science in Electrical & Computer Engineering at Queen’s University in September 2025, with an AI focus. I have eight years of online teaching experience and have held teaching assistant roles at Queen’s University and LUMS.

Lessons are available in English. I help students understand and complete their own work, prepare for school tests and develop steady study habits. Before the first lesson, please share the learner’s age or grade, current topics and the areas they find difficult.
Read more
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Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

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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.

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I really happy to teach people who will really want to part of my lesson.

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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
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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.
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Experienced Programming Language Tutor with 10+ years of teaching experience in Python, HTML, CSS, and JavaScript. I provide step-by-step, practical-oriented learning designed for beginners, students, and aspiring developers. My teaching approach focuses on real-world projects, hands-on coding exercises, and easy-to-understand explanations to help students build strong programming skills confidently.

What You Will Learn:
Python Programming Fundamentals to Advanced Concepts
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Real Projects and Practical Assignments
Debugging and Problem-Solving Techniques
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Teaching Style:
Step-by-step guidance
Beginner-friendly explanations
Practical and project-based learning
Live coding sessions
Personalized support and doubt clearing
Easy learning methods with real examples

Perfect for:

School & college students
Beginners in coding
Future web developers
Anyone wanting practical programming skills
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

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• 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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I offer Python, SQL, and computer science courses for beginners and students. We'll start with your specific goal, then work through practical exercises covering language fundamentals, SQL queries, data manipulation, and small projects. My background in Computer Science Applied to Human and Social Sciences (MIASHS) and my three years of experience in data and AI within the aeronautical sector allow me to connect theory with practice. Courses are taught in French, either online or in person, in the Toulouse and L'Union area, depending on location and availability.
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This class is for people starting from zero or close to it: school students taking informatics, first-year university students struggling with their programming course, or adults who want to learn to code and don't know where to begin.

We work through the fundamentals properly rather than rushing: variables, conditions, loops, functions, lists and dictionaries, reading and writing files, and how to break a problem into steps before you write a single line. We go at your pace, and you write the code yourself while I guide you, because watching someone else type teaches you nothing.

If you're stuck on a specific university assignment or school project, bring it and we'll work through it together. I won't do it for you, but I'll make sure you understand it.

I teach in English and Polish.
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Are you interested in learning Python but don't know where to start? This course is designed to help you understand the fundamentals of Python programming in a simple, practical, and enjoyable way.

Python is one of the most popular programming languages in the world. It is widely used in software development, automation, data analysis, artificial intelligence, and web development.

Whether you are a complete beginner, a student, or someone looking to develop new technical skills, these lessons will help you build a strong foundation in programming.

You will learn
1. Introduction to Python
2. Python Fundamentals
3. Conditional Statements and Loops
4. Working with Data
5. Functions and Practical Programming
6. Mini Projects
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Python class from basics to the advance from the students perspective with proper deep understanding of concepts.
Topics to be covered:
Basic Python and Programming fundamentals
Memory Management
OOPs
Data Structure and Algorithms
Libraries
Data analysis (using libraries Pandas, Numpy, etc.)
AI (ML, DL, RL, LLMs, RAG, Langchain etc.)

Maths using visualization of geometry, algebra, calculus, etc.
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Computer Programming is essential for nearly every child and adult. Everyday we interact with computers and applications, but we never understood how they operate or how they were made.

In this class you will learn how to create your own programs and websites, that can be used in the future for a source of income, or providing an operating application for the public.
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