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Since February 2026
Instructor since February 2026
Practical Programming & Web Development (Python, JavaScript, CSS, WordPress & UI/UX)
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From 25 € /h
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This online programming class is designed for students who want to build real technical skills in computer science and web development — not just learn theory, but actually create and understand how systems work.

I teach:

• Python programming (fundamentals, logic building, problem-solving, data handling)
• JavaScript (core concepts, DOM manipulation, interactivity)
• CSS (styling, layout systems, responsive design)
• Website Development (front-end foundations and structure)
• WordPress Development (customization, site building, content management)
• UI/UX Fundamentals (design thinking, layout clarity, user experience principles)

Students learn how to think computationally, break down problems logically, and build structured solutions. Lessons are practical and project-based, meaning students don’t just watch — they build.

For beginners, we focus on strong foundations: understanding how code works, writing clean syntax, and developing confidence in debugging.
For intermediate learners, we move into structured projects such as creating simple websites, improving layout design, adding interactivity, and understanding how front-end components connect.

My approach emphasizes clarity, structure, and application. I help students understand not only what to type, but why it works — which is the difference between copying code and truly understanding it.

This class is suitable for:
• School students exploring computer science
• Beginners transitioning into tech
• Learners preparing for academic computer science courses
• Individuals interested in web development skills

All sessions are fully online, interactive, and tailored to the student’s pace and goals.
Extra information
Classes are conducted fully online.
Students receive structured lesson plans, practice materials, and post-class feedback.
Stable internet connection and a quiet learning space are recommended.
Location
location type icon
Online from Singapore
About Me
Apprentus is built on relationships, and I strongly believe meaningful learning begins with trust and understanding. I am an experienced online educator with over 10 years of teaching experience, having worked with more than a thousand students across different countries and academic systems. Teaching for me is not just about delivering content — it is about understanding how each student thinks, learns, and builds confidence.

My classes are structured, engaging, and tailored to individual needs. Some students need step-by-step guidance to close learning gaps, others need challenge and depth to reach higher academic goals. I adapt my approach depending on the learner, ensuring that lessons are clear, interactive, and productive.

I particularly enjoy working with students who are serious about improving — whether they are preparing for exams, strengthening foundations, or aiming for academic excellence. Parents appreciate that I provide clear explanations, consistent feedback, and a supportive environment where students feel comfortable asking questions.

When students work with me, they gain clarity, structure, and confidence — not just short-term improvement, but long-term academic growth.
Education
Bachelor’s Degree in Electronic & Electrical Engineering

Strong academic background in Mathematics, Physics, analytical reasoning, and problem-solving

Continuous professional development in online education, international curricula, and digital learning tools

Experience integrating structured lesson planning, assessment strategies, and curriculum alignment across multiple academic systems
Experience / Qualifications
Over 10 years of online teaching experience across English, Mathematics, Science, and Programming

Taught more than 1,000 students globally across Asia, Europe, Africa, the Middle East, and the Americas

Extensive experience supporting students in the following curricula:

• Cambridge Primary & Lower Secondary
• IGCSE
• GCSE
• IB (MYP & DP)
• A-Level
• Common Core
• International and national school programs

Experience in external exam preparation, academic skill building, and structured lesson customization

Specialized in identifying learning gaps, strengthening foundational understanding, improving exam performance, and building student confidence

All sessions conducted online with interactive tools, structured materials, and consistent performance tracking
Age
Infants (0-3 years old)
Preschool children (4-6 years old)
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
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
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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
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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.

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* Understanding and debugging your code.

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Python is one of the easiest and most in-demand programming languages in today's job market, used in artificial intelligence, data analysis, web development, and game development. As an IT teacher with experience in school teaching, I designed these lessons to be practical and interactive:

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My name is Hassane, and I've been passionate about computers for over 20 years. With two decades of teaching experience, I've had the privilege of supporting learners of all ages and levels in developing their computer skills and achieving their professional and personal goals.

Computer science is an essential skill today, opening the door to countless opportunities. Whether you want to learn programming, website design, data analysis, or complex problem-solving, I'm here to guide you every step of the way.

What I propose:
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Computer fundamentals: master the basics to get started, Hardware, Software, Binary, Operating system.
Programming: learn to code efficiently in different languages (C#, Python, VBA).
Web development: creating modern websites and applications.
Databases: understanding, managing and analyzing data, UML, MERISE.
Project management: Ms-Project, Agil, Scrum, Kanban

Teaching methods:
I adopt a dynamic and interactive approach to ensure a rich and enjoyable learning experience:

Interactive courses: clear explanations adapted to your pace.
Practical exercises: to immediately apply the concepts learned.
Collaborative projects: developing real solutions as a team.
Personalized monitoring: answer your questions and support your progress.
Why choose me?
20 years of experience in computer teaching.
Proven methods suitable for all levels.
Personalized support to help you achieve your goals.
A passion for passing on skills that make a difference.
Whether you are a student, a professional looking to retrain, or simply curious, my courses will provide you with the tools you need to succeed in this rapidly evolving field.

Join me today!
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understanding algorithmic logic,
learn to solve problems step by step,
mastering the basics of Python,
prepare for exams, assignments and projects
progress quickly with practical exercises tailored to the student's level.

We will be working on:

variables and types,
terms,
loops,
functions,
tables/lists,
classical algorithms
programming logic,
Exercises and detailed solutions.

A clear, progressive method suitable for beginners as well as students with difficulties in computer science.

Perfect for:

students in NSI (Digital Science and Technology),
high school students,
students in BTS/BUT/Bachelor's degree programs in computer science,
beginners in programming.

Courses available in French, English or Arabic.
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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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I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples.

Topics include:
pyRevit fundamentals and setup
Automating repetitive Revit tasks (model checks, plan generation, data extraction)
Writing custom scripts for your firm's specific workflows
Applying Python automation to real MEP/BIM projects
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I work as a data consultant and hold a master's degree in Data Science and Statistics from UCLouvain. I use Python and data manipulation and analysis tools daily in a professional context.

I offer Python and data analysis courses for students, beginners and people wishing to develop practical skills in programming and data.

The lessons are tailored to your level and your goal. We can work on, among other things:

* the basics of Python and programming logic;
* variables, conditions, loops and functions;
* Data manipulation with Python and pandas;
* data cleaning and transformation;
* data exploration and visualization;
* applied statistics;
* solving practical exercises and projects;
* Understanding and debugging your code.

I prefer a practical approach: we start with a concrete problem or dataset, gradually build the solution, and analyze the errors encountered together.

The goal is to understand what you are doing rather than simply reproducing code, in order to gradually become self-reliant.

I can also support a project or student work by explaining the approach, the code and the methods used, without doing the work for you.
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Python is one of the easiest and most in-demand programming languages in today's job market, used in artificial intelligence, data analysis, web development, and game development. As an IT teacher with experience in school teaching, I designed these lessons to be practical and interactive:

Programming basics (variables, conditions, loops)
Solve realistic exercises and small projects
A simplified approach perfectly suited for beginners with no prior experience.
Suitable for students who want support with their school curriculum (IGCSE/British/American) or anyone who wants to start programming from scratch
Good-fit Instructor Guarantee
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