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Since January 2021
Instructor since January 2021
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Programming courses for beginners: Introducing young people to the fundamentals of programming
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From 13 $ /h
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Course description:

Title: Programming course for beginners
Duration: 8 weeks (16 sessions of 2 hours each)

Description:
This programming course for beginners is designed for average students who want to learn the solid foundation of programming. Whether you are interested in Python, JavaScript, HTML, CSS or PHP, this course will provide you with the essential knowledge to create your own programs and applications.

Through clear explanations, real-world examples and practical exercises, you will develop your understanding of key programming concepts and be able to apply this knowledge in real-world projects.

Lesson Plan :

Week 1-2: Introduction to programming and fundamental concepts
- Presentation of programming languages (Python, JavaScript, HTML, CSS, PHP)
- Understand variables, data types and basic operations
- Introduction to control structures (loops, conditions)
- The functions and organization of the code

Week 3-4: Object Oriented Programming
- Understand the basic concepts of object-oriented programming
- Creation of classes and objects
- Using attributes and methods

Week 5-6: Web development
- Introduction to HTML and CSS
- Creation of static web pages
- Using JavaScript to interact with page elements

Week 7-8: Applications and projects
- Using Python, JavaScript, HTML, CSS or PHP to create simple programs and applications
- Creation of a final project to put into practice all the skills acquired

Each session will include a theoretical part followed by practical exercises to consolidate the concepts covered. You will also be encouraged to ask questions and work on personal projects to reinforce your learning.

Join this exciting course and develop your programming skills, you'll be ready to create your own programs and applications in no time!
Location
location type icon
Online from Cameroon
About Me
My students call me teacher, I like sports and animals I always wake up at 5am lol. I like to teach it allows me to discover new things and to court as many people as possible and also to earn money, I like to transmit my knowledge and to discuss with the students and I like open and friendly students.
I have a very simple teaching method that pushes students to surpass themselves and achieve their goals.
Education
Computer Engineer in System and Network Administration, Scientific Baccalaureate series C, I did my studies at IAI (African Institute of Computer Science)
Experience / Qualifications
First aid certification (Red Cross), President of an association, Best Teacher 2016, Driving license
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
45 minutes
60 minutes
90 minutes
120 minutes
The class is taught in
French
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
1. Introduction to Computer Science:
- Discovery of the computer: Hardware and Software
- Basic knowledge of computing and computer use
- Exploring the user interface: desktop, taskbar, icons, etc.

2. Learning Navigation Skills:
- How to browse the Internet safely
- Discovery of search engines and web browsers
- How to create and manage favorites

3. Introduction to programming:
- Understand the concept of programming
- Learn the basics of programming with simple languages
- Use visual programming blocks to create simple programs
- Understand programming variables, loops and conditions and their use

4. Discovery of software suites:
- Use of Microsoft Office suites and Google Apps to create documents, presentations and spreadsheets
- Introduction to graphic design software such as GIMP, Canva, etc.

5. Computer Security:
- Basic security rules to protect their digital environment
- How to avoid online scams and computer viruses

6. Conclusion:
- Training summary
- Professional opportunities in IT

This study program can be adapted to meet the individual needs of children, based on their skill level and interest.
Read more
Preparation course for the official Probatory Baccalaureate and BEPC exam also intermediate class on all scientific subjects and in computer bonus.
Knowledge in outdoor classes is a plus but otherwise a brief summary of the course will be made before starting the lesson
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Hello,

I am 26 years experienced Online Tutor and Assignment Helper for Computer Science. I teach PHP, MySQL, Python, Machine Learning, Programming in C, C++, Java, ASP, C#.NET, Visual Basic, Oracle, HTML, VBScript, JavaScript, Data Structures, JQuery, Bootstrap , MS Office. I have teaching experience of teaching IT Professionals , students from different grades, graduate and post graduate classes for more than 22 years.

Presently I am teaching students online, providing homework assignment help, provide help in online tests for the students from USA ,UK, Canada, New Zealand, Germany, Australia, Malaysia, Austria, Malta, Saudi Arab etc. Also, I am in Software Development and Web Designing. I have helped more than 1200 students from different countries in last 26 years.

Regarding my teaching methodology, I always start teaching the concepts right from the scratch so that the students can learn concepts easily. I would like to describe you how do I teach through Internet . There is 100 % interaction between me and my students using Internet.

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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
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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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Hello,

I am 26 years experienced Online Tutor and Assignment Helper for Computer Science. I teach PHP, MySQL, Python, Machine Learning, Programming in C, C++, Java, ASP, C#.NET, Visual Basic, Oracle, HTML, VBScript, JavaScript, Data Structures, JQuery, Bootstrap , MS Office. I have teaching experience of teaching IT Professionals , students from different grades, graduate and post graduate classes for more than 22 years.

Presently I am teaching students online, providing homework assignment help, provide help in online tests for the students from USA ,UK, Canada, New Zealand, Germany, Australia, Malaysia, Austria, Malta, Saudi Arab etc. Also, I am in Software Development and Web Designing. I have helped more than 1200 students from different countries in last 26 years.

Regarding my teaching methodology, I always start teaching the concepts right from the scratch so that the students can learn concepts easily. I would like to describe you how do I teach through Internet . There is 100 % interaction between me and my students using Internet.

For starting the lessons with me all that you need is PC with Internet Connection and Headphone and Mic. Rest I will guide you about everything when you start learning with me. My lessons are also customized, planned and prepared according to the needs of individual student. I can also provide old student references if the student needs that.


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Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

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Deeper understanding, stronger results

• 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).
• Lessons are available in person or online.
verified badge
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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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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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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What if you could think like a hacker, so you can stop one? Let's learn cybersecurity the real way.

I'm a cybersecurity engineer with 3 years of experience and 150+ penetration tests and vulnerability assessments for government, financial, and telecom organizations. I hold CRTP, eWPT, and eJPT certifications, and I'll teach you what actually works in the field. No boring theory dumps, just live, hands-on learning.

You'll learn:

How attackers think: the hacking process from recon to exploitation
Common web attacks like SQL injection and XSS, and how to stop them
Passwords, phishing, and social engineering: why people are the weakest link
Vulnerability scanning and ethical hacking basics with Nmap, Burp Suite, and Nessus
How defenders work: firewalls, SIEM, EDR, and incident response
How to protect yourself, your team, and your business

Perfect for: beginners, career changers, students, developers, IT staff, and anyone curious about ethical hacking or preparing for Security+, CEH, or eJPT.

You'll walk away with a clear understanding of real threats, the skills to spot and fix common vulnerabilities, and a solid foundation for a cybersecurity career.

No experience needed, just curiosity and a computer. Book your first session and let's dive in!
Good-fit Instructor Guarantee
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