Introduction to Cybersecurity Course for Beginners

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From 15 Fr /h

Are you intrigued by the world of cybersecurity? This course is designed to teach you the fundamentals of web penetration testing in an accessible, straightforward, and guided way. You don't need advanced knowledge of networks or systems; we'll cover everything gradually and adapt it to your level. All you need is a desire to discover how internet security really works.

During our sessions, we will move from essential theory to practice. Together we will cover:

- Web Fundamentals: How browsers and servers communicate (HTTP/HTTPS protocols, request and response structure).
- Reconnaissance phase: techniques for gathering information about a target before analyzing it.
- We will understand and exploit common security flaws in a fully controlled manner (SQL Injections, Cross-Site Scripting (XSS), authentication problems...).
- Industry tools used by real auditors (Burp Suite, Nmap, Ffuf...).
- We will not only attack, but we will also look at the logic behind how these flaws are mitigated to protect the applications.

The course is essentially aimed at computer enthusiasts with no prior experience in cybersecurity, as well as web developers looking to understand attacks in order to write more secure code, or even vocational or university students who need a more practical approach.

My approach, as I mentioned, is 100% practical and adapted to your learning pace. All the practice is conducted within an ethical framework, using controlled testing environments, virtual machines, and laboratories, completely within the bounds of the law.

We'll start by tackling small challenges together, and as you gain confidence, I'll guide you to audit intentionally vulnerable applications on your own.

Location

Online from Spain

Age

Children (7-12 years old)

Teenagers (13-17 years old)

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Duration

60 minutes

The class is taught in

Spanish

English

Skills

Computer science

Information technology

Information systems

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

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.

Gaëlle

4.74

(27)

While adults are still debating whether kids should use AI, they are already using it.
The question isn't "should they?" it's "how do we do it intelligently?"

In this course, your child will discover:
✓ What AI actually is: not magic, not mystery. How machines think, what they can do, what they can't
✓ How ChatGPT really works: not just "ask a question and get an answer," but why it responds that way, where it fails, when to trust it
✓ What LLMs are (Large Language Models): in language they understand, not tech jargon
✓ Create with AI: custom avatars, interactive stories, real projects using real tools
✓ Think critically about AI: Bias, privacy, creativity. What does AI do better than humans? What can't it do?
✓ Real-world applications: How AI transforms medicine, education, art, gaming, everyday life

Why this is different:
Most AI courses for kids teach "here's the tool, use it." I teach how to think about AI.
Your child will learn to see AI not as black magic or a solution to everything, but as a powerful tool with real limits.
And, more importantly: that they can control how they use it.

What they take home:
Real projects they created (custom avatar, interactive app, analysis of a real AI case study). A genuine understanding of how it works. And the ability to use AI responsibly and creatively.

Format: Online | 60–90 min sessions | Flexible, adapted to their age and pace

For curious kids asking "How does ChatGPT actually know things?"

Ayoub

4.74

(27)

It is important to me to teach my students while adopting an active teaching method. I put all my experience acquired as a head teacher and also that of my academic career to the service of their success.

- Mathematics
- Chemical Physics,
- Technology.

My courses are aimed at students in the French system from 6th to 12th grade (Speciality: Mathematics, PC).

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! :)

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)

Learn calculus from an instructor with proven experience tutoring advanced calculus at the college level. This course combines rigorous academic standards with clear, student-centered explanations to help learners truly understand challenging concepts.

With experience guiding university students through advanced topics, the instruction is practical, thorough, and focused on long-term mastery—ideal for students pursuing mathematics, engineering, or science.

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).

Ali

4.74

(27)

I offer one-to-one Machine Learning and AI tuition for university students, postgraduates, working professionals, and serious self-learners. Lessons are available online or in person around Birmingham.
What I cover:

Python for data science and ML (NumPy, Pandas, Scikit-learn)
Deep learning with TensorFlow and Keras
Core ML concepts: regression, classification, clustering, neural networks, CNNs
Computer vision and image classification (my published research area)
University coursework support, dissertation help, project guidance
Help with Kaggle competitions and personal portfolio projects

How I teach:
I focus on understanding, not memorisation. We work through real datasets and real problems — not toy examples — so you can actually apply what you learn. I'll help you build a model from scratch, debug it when it doesn't work, and explain the maths behind why it does or doesn't perform well. For university students, I can also help with assignments, dissertations, and final-year projects.
Whether you're just starting out, stuck on a coursework project, or trying to break into ML professionally, I can meet you wherever you are and help you move forward.

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.

Abdelmalek

4.74

(27)

Master Industrial Control Systems (SCADA, DCS, IIoT) and automation through tailored, hands-on coaching based on real-world industrial projects! With over 10 years of international engineering, solution architecture, and technical business development experience working with major industry vendors

I offer practical courses designed for engineering students, university undergraduates, and professionals looking to upskill.

The pathway will be in 7 days to cover all basics in OT environnement :
Day 1 - Virtual Environment Preparation for OT projects
- Install Hypervisor on your workstation (A virtual machine).
- Create a Linux VM (Fedora Server).
- Configure 2 networks on the VM: one in NAT (internet access) and one in Host-Only Network (to isolate lab traffic).
- Install basic tools for OT
Day 2: Modbus PLC Simulation (Add 2 Server and test script client to connect)
- Implement Modbus PLC simulators and architecture overview
- Create PLC simulator scripts in src/plc-simulators/
- Add validation test script for Modbus connectivity
- Update Day 2 guide with detailed implementation steps and compatibility notes
Day 3: NGINX Load Balancer Configuration (Round Robin)
- Understand NGINX Stream Module
- Configure NGINX
- Verify and Load the Module
- Troubleshooting NGINX (Activate load balancing in layer 4 protocol, Set permission)
- Step-by-Step Load Balancer Validation
- Test Load Balancing (Round-Robin)
- Test Failover (Resilience)
Day 4 - Creation of the traffic generator (SCADA Client)
- TBD
Day 5 - Traffic capture and measurement with TShark
TBD
Day 6 - Advanced analysis and overload simulation
TBD
Day 7 - Grafana
-TBD
What we can cover together based on your goals: