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

From 12 £ /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

12 £ /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

12 £ /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.

Robert

4.74

(27)

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.

Khalil

4.74

(27)

If you’ve ever felt that science and math are difficult, it’s probably because no one showed you how to think like a problem solver.
In my classes, you’ll learn not just formulas or code but how to truly understand concepts, apply them, and build strong logical intuition.

I teach:
• 🔢 Mathematics: From algebra and calculus to applied problem-solving for real-world use.
• 💻 Computer Science: Coding fundamentals (Python, C++), algorithms, and logical thinking for beginners and intermediate learners.
• ⚛️ Physics: Mechanics, thermodynamics, and practical examples that make abstract ideas simple and visual.

As a Software Engineer and Master’s student in Engineering at Nagoya University, I bring both academic knowledge and hands-on experience from real projects. My teaching approach is interactive, visual, and deeply focused on understanding over memorization.

Let’s turn complex problems into clear, step-by-step insights — and make learning something you genuinely enjoy.

Antonin

4.74

(27)

Need a catch-up, tutoring, private lessons or help with homework in mathematics? In computer science ? In logic?
I'm here for you!
I offer you a personalized approach; because there is no one method that works for everyone, I adapt to the needs and requests of each student (and their parents). The first hour of class will be used to define the student's needs, deadlines and strengths.
My courses are aimed at secondary school students of all levels, higher education students and anyone wishing to refresh or strengthen their knowledge of mathematics and computer science. I have been helping friends and acquaintances on a voluntary basis for a long time in the success of their studies and I hope to be able to put this experience to the benefit of your success :)

Reza

4.74

(27)

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! :)

Amine

4.74

(27)

Discover programming lessons suitable for children! With a fun and educational approach, my lessons allow young minds to dive into the fascinating world of programming. Provide your children with an enriching learning opportunity in a fun and stimulating environment.

Florian

4.74

(27)

Experienced and patient teacher of logic for computer science.

I have taught logic, formal languages and automata theory to undergraduates for six years. My tutoring is adapted to the student's level and goals. Whether you need to learn logic for your studies, or you would simply like to know more about the subject, I will be more than happy to help you improve your understanding and skills.

Logic
The sciences presuppose a certain standard of rationality. An ability to distinguish between correct reasoning and claims that do not follow from the assumptions. In this class we study the basic principles of logic and apply mathematical techniques to the study thereof.
Topics include:
Propositional and Predicate Logic
Syntax and semantics
Natural deduction
Semantic tableaux
Correctness and soundness
Completeness

Formal languages and automata
A formal language is an abstraction of general characteristics of programming languages. Such a languages consists of a set of symbols together with some rules to determine whether a string made up out of those symbols is a member of the language.

Topics include:
Regular languages, context-free languages
Finite automata, pushdown automata, Turing machines
Regular expressions
Regular grammar, context-sensitive grammar
Pumping lemmas for regular and context-free languages

Baia

4.74

(27)

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

4.74

(27)

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

4.74

(27)

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.

Mohamed

4.74

(27)

This fun and interactive course introduces kids and teens to the fundamentals of coding using Scratch, a visual programming language developed by MIT. Students will learn to create games, animations, and interactive stories while developing problem-solving, logical thinking, and creativity skills—all in a playful, drag-and-drop environment!
Who Should Join?
✔ Kids & teens curious about coding and game design
✔ Young learners who enjoy storytelling, art, or technology
✔ Future coders looking for a fun introduction to programming

Nicolas

4.74

(27)

I am a dynamic and demanding teacher who gives private lessons in Physics-Chemistry as well as Mathematics.

I graduated from teaching seven years ago, after a masters in physical sciences with honors, and I teach in college and high school since.
I have also been preparing students for the Baccalaureate Science for many years, all of whom have been awarded very good honors.
I also prepare my students for different exams (Matu, Bac, preparation for EPFL, etc...)

I make sure to rework the basics so that the student can progress quickly. It is important to me that my students acquire a solid foundation of knowledge.
I also give effective work methods that will allow him to progress much more quickly and so he can regain self-confidence.

I can travel to the student's home or also conduct the lesson via Zoom/Google Meet.

Martin

4.74

(27)

Working part-time in the watch industry, I have been tutoring for several years in the context of refresher, occasional support or preparation of exams or competitions. Very experienced in relation to the difficulties encountered by students and pedagogue, I adapt to the needs of each to quickly regain the necessary confidence, the methodology of mathematical reasoning and allow a rapid improvement of results.
Experienced and pedagogue, I adapt to the needs of the student to help him consolidate his knowledge methodically, to regain confidence and improve as quickly as possible its results. I teach these courses in a radius of 30 km around Geneva.

Moumita

4.74

(27)

This class is perfect for students who are new to programming and want to learn how to code in Java and Python. We will start from the basics and slowly build up confidence by learning how programs work, how to write simple code, and how to solve problems step by step. Lessons include easy examples, practice exercises, and clear explanations. No previous coding experience is needed—just curiosity and willingness to learn.

Léo

4.74

(27)

• Teaching methodology and techniques: I favour a personalized approach, adapting the courses according to the profile and academic background of each student.
• Typical course structure: tutoring in economics, econometrics, statistics and probability, financial mathematics, trading, investment, or political economy. Courses can take place at home, via videoconference, or at a pre-selected location, ideally quiet, free, and conducive to learning.
• Specifics as a teacher: I offer support throughout the school year, with free corrections of exercises outside of class, regular availability, and the guarantee of being accessible until the end of the year, subject to the general conditions of Superprof.
• Target audience: all levels, regardless of diploma, class or specific characteristics.
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The path and the method are in there; take a little bit of each.

Holding a degree in mathematics from EPFL, I offer private lessons in Geneva or online.

I graduated from EPFL with a degree in mathematics, having completed all the Bachelor's level courses in this discipline. I have gained significant experience tutoring students from middle school to university level (mathematics and physics). I have also assisted with teaching at EPFL, particularly in specialized courses such as analytic geometry (advanced mathematics course), analysis (first and second year Bachelor's level), and linear algebra (first year Bachelor's level). My in-depth mastery of the theory in these disciplines provides me with the skills and teaching abilities necessary to effectively support high school and university students, helping them understand the theoretical concepts in their courses and apply them practically in their exercises.

Typical course: a quick review (adapted to needs) of the essential concepts of the course, followed by practical exercises and oral role-playing (going to the board, discussion on the physical meaning, etc.), as in a competitive oral exam.

All my lessons are prepared in advance based on the topics covered in class (the student specifies their needs from one session to the next). I also create a handout containing sample exercises illustrating different methods, fully corrected and explained by me.

My commitment to my students' success is absolute. I only prioritize motivated students who are ready to put in the necessary effort to progress.

My main focus is on in-depth understanding and the quality of work. Depending on the student's request, I can also suggest exercises to do between sessions (not mandatory, depending on available time and homework already assigned by their school).

Ammar

4.74

(27)

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.