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Since August 2024
Instructor since August 2024
Learn Programming Easily – Python, Java, C++, Web & Algorithms
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From 20 £ /h
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I am a Computer Science Engineer specialized in Artificial Intelligence, with a Master’s degree in Software Engineering and several years of experience in private tutoring.

I offer personalized programming and computer science lessons for beginners, school students, university students, and adults.

I can help you with Python, Java, C++, C#, JavaScript, HTML/CSS, SQL, algorithms, databases, and the fundamentals of Artificial Intelligence.

My lessons are adapted to each student’s level and goals. I explain concepts step by step using clear examples, practical exercises, and real programming problems. I can also help with assignments, university projects, exam preparation, and understanding difficult programming concepts.

My goal is not only to help you solve exercises, but also to make sure you understand the logic behind them and become more confident and independent in programming.

Lessons are available in English, French, or Arabic.
Extra information
Before the first lesson, please send me your current level, the programming language or topic you would like to study, and, if possible, the exercises, course materials, or project you need help with.

Please have a computer available during programming lessons so that we can practice together.
Location
location type icon
Online from Lebanon
About Me
I am a dedicated Data Scientist and educator with extensive experience in tutoring mathematics, data science, and computer science. With a Master's degree in Computer Science and a Bachelor's degree in Mathematics with a focus on Data Science, I bring a strong academic foundation and practical expertise to my teaching.

My professional background includes working as a freelance tutor since 2019, where I’ve successfully guided university students in programming, data analysis, and mathematical concepts. I’ve also worked as an AI Developer, contributing to innovative projects such as early detection algorithms for brain cancer.

I offer personalized tutoring sessions online via Zoom and Google Meet, and in-person when possible. My teaching approach emphasizes clear explanations, practical problem-solving, and tailored support to meet individual learning needs. I am committed to staying updated with the latest technologies and methodologies to provide the most effective learning experience.
Education
Master 2 of Software Engineering, Lebanese University, Lebanon
Master 1 of Computer Science, Lebanese University, Lebanon
Bachelor of Mathematics, Data Science Option, University of Saint-Joseph, Lebanon (GPA: 84/100)
CCNA 2 Certificate, CISCO
Experience / Qualifications
Freelancer, Tripoli, Lebanon (January 2019 - Present)
Tutor in mathematics, data science, and computer science, providing comprehensive support for university students. Developed personalized teaching methods, resulting in significant student improvements and high satisfaction rates.

AI Developer, MRIMath LLC, Remote (April 2023 - September 2023)
Created advanced Python algorithms for early brain cancer detection. Collaborated with developers and medical professionals to design and implement precise AI solutions, exceeding industry benchmarks.

Coding Teacher for Kids
Conducted Scratch coding classes at BrightChamps and Schola.pro, both online and in-person. Provided tailored one-on-one coding lessons to foster a personalized learning experience.

University Private Tutor, Launch Company LLC and Study Buddy LB, Remote
Offered individualized tutoring in computer science, data science, and mathematics. Adapted teaching strategies to meet diverse student needs.

Qualifications:

Master of Computer Science, Lebanese University, Lebanon
Bachelor of Mathematics, Data Science Option, University of Saint-Joseph, Lebanon (GPA: 84/100)
CCNA 2 Certificate, CISCO
Skills: Proficient in Python, R, Java, SQL, C, C#, Matlab; experienced in project management, data visualization, AI development, and version control with GitHub.
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
French
English
Arabic
Availability of a typical week
(GMT -05:00)
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
Experienced tutor in coding (Java, C#, Python, C++, HTML, CSS, JS, Matlab) and maths (stat, proba, algebra, analysis), I offer online courses via Zoom/Google Meet. Expert in problem solving, data analysis, and algorithm design, I support you with practical exercises and preparation adapted to all levels.
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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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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.

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You have a kid that's interested in programming?? Set up a class for him with me. Aya a software engineer turned teacher. I was a software engineer for 3 years before I joined the teaching industry and I currently teach computer science in Chichester College in United Kingdom. I want to advocate for computer science and help familiarise kids at a young age with programming in Python.

Send me a message let's chat about what you want your kid to learn and what I can offer! :)
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Learn Python by building something you’ll actually use

Ever wanted to code but had no idea where to begin? You’re in the right place. In these relaxed, one-on-one lessons, you’ll go from zero to writing real programs, with no experience required.

Together, we’ll choose a project that fits your interests. Maybe it’s a video game you want to build or a quiz to challenge your friends, a chart built from your own spreadsheet, or a script that takes over a boring, repetitive task. Along the way, you’ll pick up the essentials (variables, loops, functions) and learn to read error messages without panic, so you can change your code with confidence.

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AI-assisted coding: Use tools like ChatGPT to brainstorm, decode errors, and improve your code, while learning to test its suggestions and explain how the final result works.
Machine learning: Train a simple model on a real dataset and see how well it predicts.
Your own project: Bring an idea from school, work, or a hobby, and we’ll break it into steps you can build at your own pace.

Your instructor

I’m an engineer with a master’s degree from Nagoya University and research experience in applied AI and computer vision. I explain every concept in plain language, then give you time to try it yourself with live feedback, so you’re never stuck alone.

Ready to start?

Send me a quick message and let's do your first coding project together.
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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.

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This comprehensive and personalized academic support program is designed to meet your needs and help you succeed!

💡 Why Choose this Program?
This course offers tailor-made support, adapted to your level and your objectives:

🎯 Progress in mathematics by strengthening your foundations and mastering advanced concepts.
💡 Understand theoretical concepts in depth to better apply them in exercises and problems.
💻 Learn to program in C, C++, Python and Java with clear explanations and practical exercises.
🚀 Develop essential skills in algorithms and computer problem solving.
🎓 Effectively prepare for your exams (Bac, Licence, competitive exams) thanks to targeted revisions and practice subjects.
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Mathematics is the key to academic success in many scientific and technical fields.
This module covers:

Secondary Level (Middle and High School):

Arithmetic, fractions, percentages, proportionality.
Algebra: Equations, inequalities, functions (linear, quadratic, exponential, logarithmic).
Geometry: Theorems, trigonometry, analytical geometry.
Statistics and probability: Analyze data, calculate probabilities.
Preparation for exams: Brevet, Bac, entrance exams for grandes écoles.
University Level (Bachelor):

Differential and integral calculus: Derivatives, integrals, sequences and series.
Linear Algebra: Matrices, vectors, systems of linear equations.
Advanced Probability and Statistics: Random variables, probability laws, estimation and hypothesis testing.
Numerical analysis: Methods for approximate resolution of equations and systems of equations.
Discrete Mathematics: Graphs, Boolean logic, combinatorics.
This module offers progressive exercises, clear explanations and detailed corrections to understand in depth and train effectively.

💻 Programming – C, C++, Python and Java
Mastering programming is a major asset for success in the digital and technological field.
This module covers the fundamentals of programming to enable you to:

Understand algorithmic logic and computer problem solving.
Master the syntax of the C, C++, Python and Java languages.
Writing your first programs: Variables, conditional structures, loops, functions.
Work on practical projects: Calculator, data management, simple games, sorting and searching algorithms.
Develop advanced skills:
Object-oriented programming (C++, Java): Classes, inheritance, polymorphism.
Memory management (C, C++): Dynamic allocation, pointers.
File manipulation: Reading and writing data.
Data structures: Lists, stacks, queues, binary trees.
Code optimization for faster and more efficient programs.
This module offers concrete examples, practical exercises and motivating projects to help you learn while having fun while developing skills useful in the professional world.

🎯 Interactive and Motivating Teaching
Dynamic online courses: Learn from home in an interactive format with audio and screen sharing.
Tailor-made method: The courses are designed according to your level and your objectives for learning at your own pace.
Practical exercises and concrete projects: To apply theoretical concepts and develop your skills.
Personalized monitoring: Regular support to monitor your progress and adapt the program to your needs.
Encouragement and motivation: A positive approach to building your confidence in your abilities.
🔔 For Who?
This program is aimed at:

High school students wishing to strengthen their foundations in mathematics or learn to program.
University students in science or computer science looking to deepen their knowledge of math and programming.
Candidates for exams and competitions preparing for the Baccalaureate, a License, or entrance exams to the grandes écoles.
Programming enthusiasts wanting to learn the fundamentals of C, C++, Python or Java.
Adults in professional retraining wishing to acquire programming skills.
🚀 Ready to Succeed?
Join the "🎓 Academic Support – 📘 Maths from Secondary to Bachelor & 💻 Programming in C, C++, Python and Java!" and benefit from personalized support to achieve your goals.
Whether you want to improve your grades, pass your exams or develop programming skills, this program will give you knowledge, confidence and motivation.

Register today and take the first step towards success!
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Students can choose between website or mobile app development.Hands on dev
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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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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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You have a kid that's interested in programming?? Set up a class for him with me. Aya a software engineer turned teacher. I was a software engineer for 3 years before I joined the teaching industry and I currently teach computer science in Chichester College in United Kingdom. I want to advocate for computer science and help familiarise kids at a young age with programming in Python.

Send me a message let's chat about what you want your kid to learn and what I can offer! :)
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Learn Python by building something you’ll actually use

Ever wanted to code but had no idea where to begin? You’re in the right place. In these relaxed, one-on-one lessons, you’ll go from zero to writing real programs, with no experience required.

Together, we’ll choose a project that fits your interests. Maybe it’s a video game you want to build or a quiz to challenge your friends, a chart built from your own spreadsheet, or a script that takes over a boring, repetitive task. Along the way, you’ll pick up the essentials (variables, loops, functions) and learn to read error messages without panic, so you can change your code with confidence.

Where you can go next

AI-assisted coding: Use tools like ChatGPT to brainstorm, decode errors, and improve your code, while learning to test its suggestions and explain how the final result works.
Machine learning: Train a simple model on a real dataset and see how well it predicts.
Your own project: Bring an idea from school, work, or a hobby, and we’ll break it into steps you can build at your own pace.

Your instructor

I’m an engineer with a master’s degree from Nagoya University and research experience in applied AI and computer vision. I explain every concept in plain language, then give you time to try it yourself with live feedback, so you’re never stuck alone.

Ready to start?

Send me a quick message and let's do your first coding project together.
Good-fit Instructor Guarantee
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