Cybersecurity Essentials – Theory and Hands-On Labs (Kali Linux)

From 22 C$ /h

This course introduces fundamental concepts of cybersecurity, covering theoretical knowledge and practical laboratory experience.

Students will learn essential cybersecurity principles including security threats, protection mechanisms, network security basics, and ethical security practices.

The course combines theory with hands-on practical training using Kali Linux, which is commonly used for security testing and learning cybersecurity tools.

Topics include understanding common cyber threats, basic defensive techniques, and introduction to security tools and environments. The course is designed to help students build awareness of cybersecurity concepts and develop practical skills.

Teaching is structured to provide clear explanation of theoretical foundations followed by guided practical exercises.

📚 Topics Covered

• Introduction to cybersecurity concepts
• Types of cyber threats and vulnerabilities
• Basic network security principles
• Ethical security practices
• Security awareness fundamentals
• Practical labs using Kali Linux tools
• Introduction to penetration testing concepts

Extra information

💻 Students need a laptop for practical exercises.
✨ This course is suitable for beginners interested in cybersecurity.

Location

Online from Lebanon

About Me

With experience delivering more than 2500 coding and technology online training sessions across the UK, USA, Egypt, and Gulf countries, I have worked with students from different backgrounds and skill levels, helping them improve their technical knowledge and practical skills.

I am a university instructor specializing in computer science and information technology education. I have experience teaching and explaining complex technical concepts in a clear, structured, and practical manner.

I offer tutoring and training in programming languages including C++, Java, Python, and C#, as well as web development. I also teach computer networking, data communication, cybersecurity fundamentals, artificial intelligence (machine learning), robotics and introductory IoT concepts.

My teaching approach focuses on understanding core principles, solving practical problems, and applying knowledge through examples and exercises. I adapt my lessons according to the student’s level, whether beginner or advanced.

I am committed to helping students build strong technical foundations, improve their problem-solving skills, and gain confidence in working with technology.

All sessions are delivered in a supportive learning environment where questions and discussion are encouraged.

Education

• Bachelor’s Degree in Computer Science – GPA: 3.81
• Master’s Degree in Computer Science – GPA: 3.90
• Certified in Machine Learning Fundamentals and Engineering from IBM.
• Certified in Computer Fundamentals from ICDL program.

Experience / Qualifications

• University instructor with extensive experience in teaching computer science and information technology subjects.
• Delivered more than 2500 coding and technology training sessions to students across the UK, USA, and Gulf countries.
• Experienced in teaching programming languages including C++, Java, Python, and C#.
• Knowledgeable in computer networking, data communication, cybersecurity fundamentals, artificial intelligence basics, IoT, and robotics concepts.
• Skilled in explaining complex technical topics in a simple, structured, and practical manner.
• Focused on helping students develop problem-solving skills and strong technical foundations.

Age

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

Duration

60 minutes

The class is taught in

English

Skills

Computer science

Security

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Advanced Programming with Python – OOP, Data & Algorithms

22 C$ /h

This advanced course builds on programming fundamentals using Python (programming language) and is designed for students who want to deepen their programming knowledge.

The course continues from functions and introduces advanced programming concepts including object-oriented programming principles such as polymorphism, inheritance, abstraction, and encapsulation.

Students will also learn data handling techniques, working with Python libraries, and developing structured programs using complex loops and data collections.

The course covers practical implementation of nested loops, nested lists, tuples, and dictionaries, as well as an introduction to data structures and algorithmic thinking.

Additional topics include graphical user interface development using libraries such as Tkinter, along with introductory concepts in data science and machine learning using Python.

Teaching combines theoretical explanation with real coding exercises to help students develop strong practical programming skills.

• Review of programming fundamentals and functions
• Object-Oriented Programming (OOP) concepts
• Polymorphism, inheritance, abstraction, and encapsulation
• Data structures basics
• Nested loops and complex data handling
• Lists, tuples, and dictionaries
• Introduction to algorithms
• Working with Python libraries
• GUI development using Tkinter
• Introduction to data science and machine learning concepts

Computer Basics & Productivity Skills – Fundamentals, OS, and Office Applications

19 C$ /h

This course is designed for complete beginners who want to build strong basic computer skills and understand essential computing concepts.

Students will learn computer fundamentals including hardware components, input, processing, and output concepts, as well as basic operating system operations such as file management and system handling.

The course also introduces practical productivity skills using Microsoft Word, Microsoft Excel, Microsoft PowerPoint, Microsoft Outlook, and Microsoft OneDrive.

The teaching approach combines explanation and practical exercises to help students confidently use computers for academic and everyday tasks.

This course is ideal for beginners who want to start learning computer science concepts and improve their computer productivity skills.

Moetez

5.0

(2)

Are you interested in the world of the Web, but you don't know anything about it? Do you get confused when you hear geeky vocabulary? This course aims to bring you up to speed. No prior knowledge is required. We're going to touch on something pretty simple here.

At the end of this course, you will be able to:
- Defining the web and its origins
- Distinguish the different languages of the Web.
- Familiarize yourself with the 3 basic web development technologies: HTML, CSS and Javascript
- Explore other technologies such as Jquery, Bootstrap, Ajax

For more information, do not hesitate to contact me!

cordially

Robert

5.0

(2)

As a Franco-Belgian management teacher, I give Excel lessons with passion!
Whether remotely or face-to-face, I offer many examples and exercises to accompany you.
I travel without problem throughout the region of Brussels and its surroundings, for lessons of at least 2 hours. For France, courses are only given remotely.

Here are some key words that will be covered in my classes:
Scenario analysis, Year, Rounding, Today, Bdnb, Bdnbval, Bdsum, Search, Column, Copy/paste in values, Copy/paste with transposition, Consolidation, Date, Datedif, Determat, Dollar, Right, Righterg, Equiv, Esterror, Estna, Frequency, Filter (simple and advanced), Format of cells, Left, Large.Value, Printing of documents, Index, Indirect, Inversemat, Day, Weekday, Line, Matrix, Max, Maxa, Max.Si, Min , Mina, Mina.If, Formatting of cells and ranges, Month, Average, Average.If, Nb, Nb.If, Nbval, Naming of cells and ranges, No, Small.value, Product, Productmat, Protection of cells, Lookup (Lookup), Lookupv (VLookup), Lookuph (HLookup), If (If), If.Not.Disp, If.Conditions, Iferror, Sum, Sumproduct, Sum.If, Sum.If.Set, Substitute , Pivot tables, Sorting, Cell locking

Do not hesitate to contact me to organize your lessons according to your needs and availability. Together, we will develop your Excel skills in an efficient and personalized way.

Amin

5.0

(2)

Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance.
Whether you've never written a line of code or you're a student who needs to pass a programming course, this is a practical, no-fluff introduction that gets you writing real code from session one.
What we can cover depending on your goals:

Python fundamentals: variables, loops, functions, data structures
- Object-oriented programming (OOP)
- Data manipulation with pandas and NumPy
- Introduction to machine learning with scikit-learn
- Database management with SQL
- C and Java upon request
- MATLAB and R available for engineering/science students

Why learn with me?
I'm not a student teaching on the side — I'm a professional engineer who uses Python daily for data analysis, modeling, and automation. I know exactly which concepts matter in the real world and which ones you can skip for now.
Sessions are 100% personalized: I adapt the pace, the examples, and the exercises to your background and your goal — whether that's passing your university exam, building a project, or landing a job.

Reza

5.0

(2)

You will learn Systematic Reasoning & Logical Thinking which is a requirement for entering Computer Science program in many universities.
The book “Delftse Foundations of Computation” especially its second chapter will be the main source of our lesson, but other more in-depth books will be also covered if you want to improve even further on logical thinking.
The topics in our lesson include:
• Propositional Logic: Logical operators; Precedence rules; Logical equivalence; Implications in English; Exclusive or; Universal operators; Classifying propositions
• Boolean Algebra: Substitution laws
• Logic Circuits: Logic gates; Combining gates to create circuits; From circuits to propositions; Disjunctive Normal Form; Binary addition.
• Predicate Logic: Predicates; Quantifiers; Tarski’s world and formal structures;
• Deduction: Valid arguments and proofs; Proofs in predicate logic

If you have any additional questions before starting a class, please feel free to ask me. I am here to assist! :)

Nadeesha

5.0

(2)

I specialize in teaching ICT, computer science, and programming to help students develop essential digital skills for today's tech-driven world. My lessons are designed to guide you through both foundational concepts and more advanced topics, depending on your current level. I teach you very easy methods to understand complex parts of programming, breaking down difficult concepts into simple, digestible steps. Whether you’re a complete beginner looking to understand basic computer operations or an advanced learner aiming to master coding, networking, or system administration, I tailor each lesson to your needs. You will work on real-world projects, and I will provide exercises to reinforce learning and track your progress. Let's build your technical expertise together!

Baia

5.0

(2)

I teach Python specifically for finance and data applications - the kind used in economics, business analytics, and quantitative programs. This isn't a general "learn to code" course; it's built around real financial data, benchmarking, and the workflows you'll actually use in coursework or early career work.

Topics include:
Python fundamentals through a finance lens (data structures, functions, control flow).
Working with financial data and datasets.
Performance benchmarking and writing efficient code.
Applying concepts from Hilpisch's Python for Finance.

Vincent

5.0

(2)

With over seven years of experience in teaching Computer Science & Information Technology (ICT), I have developed a strong expertise in delivering high-quality education across multiple internationally recognized curricula, including Cambridge IGCSE, GCSE, A-Levels, O-Levels, and Checkpoint. My passion lies in equipping students with coding, cybersecurity, and digital literacy skills, ensuring they are well-prepared for the evolving demands of the digital world.

Expertise & Teaching Areas:
✅ Programming & Software Development: Python, Java, C++
✅ Cybersecurity: Ethical hacking, data protection, network security
✅ Digital Literacy: ICT applications, online safety, cloud computing
✅ Data Science & AI: Data analysis, machine learning fundamentals
✅ Web Development: HTML, CSS, JavaScript

Curriculum & Pedagogical Experience:
🔹 Cambridge IGCSE & GCSE ICT & Computer Science – Teaching core and extended syllabi, focusing on programming logic, databases, and networking.
🔹 Cambridge A-Levels & O-Levels Computer Science – Preparing students for advanced computing concepts, problem-solving, and algorithm development.
🔹 Cambridge Checkpoint ICT – Building foundational skills in digital technology and computer applications.

Professional Impact:
📌 Mentored students to achieve top grades in Cambridge ICT & Computer Science exams.
📌 Developed interactive lesson plans integrating real-world applications of technology.
📌 Conducted coding boot camps and cybersecurity workshops to enhance practical learning.
📌 Guided students in project-based learning, including app development and website design.

With a strong commitment to student-centered learning and technological innovation, I am dedicated to shaping future tech leaders and empowering learners with skills relevant to careers in technology, data science, and software development.

Dr.Ibrahim

5.0

(2)

This course provides a foundational understanding of Information Technology, data centers, covering architecture, power & cooling, networking, storage, virtualization, security and lots more. Learn best practices for efficiency, scalability, and reliability while exploring emerging data center solutions. Ideal for IT professionals, engineers, and facility managers involved in data center deployment or management.

This course offers a comprehensive exploration of Information Technology, data center infrastructure, guiding students through the entire lifecycle—from initial design and planning to day-to-day operations and long-term performance optimization. Students will learn the critical components of data center design, including site selection, power and cooling systems, space planning, networking, and physical security. The course also covers operational best practices, monitoring tools, energy efficiency strategies, disaster recovery planning, and emerging trends. By integrating technical, environmental, and management perspectives, students will gain the knowledge and skills required to build and maintain high-performance, cost-effective, and sustainable data center environments.

Dr.Ibrahim

5.0

(2)

This course introduces students to the fundamentals of Information and Communication Technology (ICT) and its role in modern society. Topics include computer hardware and software, digital communication tools, internet technologies, data management, cybersecurity, and emerging trends. Students will gain practical skills in using productivity software, conducting online research, and understanding the ethical and responsible use of digital resources. The course emphasizes both technical proficiency and digital literacy, preparing learners to confidently navigate and contribute to a technology-driven world.

Adam

5.0

(2)

🐍 Python Course – Learn to code and create your projects!

This course is for anyone who wants to:

✅ Learn Python from the beginning
✅ Strengthen their programming skills

📚 On the program:

Variables

Loops

Functions

Data structures

Practical projects for implementation

💡 How does the course work?

Clear explanations to understand the programming logic

Targeted exercises adapted to your level

Concrete projects to create your own applications

🎯 My goal:

Helping you understand the logic behind the code

Progress at your own pace

Create your own projects in Python and gain independence

Ammar

5.0

(2)

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
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.

Agnel

5.0

(2)

Whether you're preparing for IB, IGCSE, GCSE, or simply want to build confidence in Mathematics, I'm here to help. I have over 3 years of teaching experience and 7 years of professional software engineering experience, allowing me to explain mathematical concepts in a clear, logical, and practical way.

My lessons are tailored to each student's needs and learning pace. I believe understanding the "why" behind a concept is far more valuable than memorising formulas. During lessons, I use carefully prepared practice questions and exam-style exercises, many of which I create myself using professional typesetting tools like LaTeX that is used by IB and Cambridge directly.

I encourage students to ask questions freely and develop strong problem-solving skills. Whether you're aiming to improve your grades, prepare for school exams, or study for international curricula such as IB, IGCSE, or GCSE, I'll help you build confidence step by step in a supportive learning environment.

I use a Pen Display Tablet to write on it using Goodnotes as the writing app, which I share the link to the whiteboard used with the students.

Lessons are available online or in-person (within 30 mins train travel from Shinjuku, Tokyo), and are conducted in English.

Yassine

5.0

(2)

I am a certified computer science professor who helps graduates and students with exam retakes and competitions. I tutor preparatory classes (MPSI, MP, PSI, ECS, etc.) up to university level (Bachelor's & Master's in Science or Economics). My method is based on understanding the lessons, practicing correctly, organizing the concepts, and completing exercises and problems of your choice. Each session includes verbal exercises, methodological tips, and subsequent personalized advice. You will receive a video recording and an annotation in PDF format after each session. The online courses are conducted via Google Meet, 5 days a week, with flexible scheduling. I am available between sessions to answer questions. Contact me for an initial consultation.

Mariam

5.0

(2)

This cohort is designed for young people who want to learn in an affordable, flexible, and enjoyable way without having to dedicate a huge amount of time each week or even just extra support.

This beginner-friendly course introduces students to the world of computers and computer science through simple explanation.

Students will learn how computers work, including hardware, software, memory, storage, data, and how a computer processes information. They will then explore how applications are used to create and organize information, with practical experience using tools such as Microsoft Word, PowerPoint, and Excel.

As the course progresses, students will be introduced to important computer science concepts including binary numbers, algorithms, programming, databases, networks, the Internet, and cybersecurity.

By the end of the course, students will have a good foundation in computer science and improved digital skills.

Sara

5.0

(2)

As a Master's student in Data Science at EPFL, a graduate of CentraleSupélec (ranked in the top 3% of my class) and holder of a Bachelor's degree in microtechnology from EPFL, I offer tutoring in mathematics, physics and computer science, from primary school to university level.
My teaching experience
I was a student teaching assistant at EPFL for 8 courses, working with over 400 students. I currently lead the linear algebra and ICC (Information, Computation, Communication) exercise sessions. Each week, I adapt my explanations to each student's level: that's what I love most about teaching.
What I propose
• Primary and secondary school: consolidate the basics (calculation, fractions, geometry, equations), regain confidence and improve methodology.
• Gymnasium / high school (maturity, baccalaureate): functions, analysis, probabilities, vectors, mechanics, electricity, exam preparation.
• University / EPF / preparatory classes: analysis, linear algebra, probability and statistics, numerical analysis, programming (Python, C/C++).
My method
I begin by identifying the real obstacle: a misunderstanding of the concept, a lack of methodology, or stress. Then, I build the sessions based on the student's lessons and exercises. The goal isn't just to pass the next test, but to understand the material and become independent.
Whether you need occasional homework help, regular support, or intensive exam preparation, I adapt to your needs.