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Since February 2026
Instructor since February 2026
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JavaScript from Scratch - Professional Practical Course
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From 29 € /h
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Do you want to learn to program from scratch and create real web applications?
In this course you will learn JavaScript, the fundamental language of modern web development, used by millions of developers worldwide.

You don't need any prior programming experience. We'll start with the basics and progress step by step until you're able to create interactive projects on your own. If you have prior experience, we can start where you're comfortable or where you need assistance.

What will you learn?
Programming Fundamentals
Variables, data types, and operators
Conditionals and loops
Reusable functions and structures
Arrays and objects
DOM Manipulation
Events and forms
Introduction to asynchrony (promises and async/await)
API consumption
Development of practical projects

Methodology
The course is 100% practical:
Live programming
Guided exercises
Mini-projects in each module

The goal is not only to understand the theory, but to learn to think like a programmer and acquire a solid foundation to move towards modern frameworks like React or Node.js.

Who is it addressed to?
People with no prior experience
Students who want to strengthen their programming
Professionals who want to get into web development
Anyone interested in the world of technology

At the end of the course you will be able to create interactive web applications and you will have a solid foundation to continue advancing in frontend or backend development.
Extra information
Having Visual Studio Code and a good internet connection is enough to start with.
Location
location type icon
Online from Spain
About Me
🧑‍💻 About me
I'm a software developer passionate about technology, specializing in programming and systems administration. Since I started in IT, I've enjoyed solving real-world problems with code, learning firsthand how a system works internally, and constantly advancing in areas like systems administration, scripting, and cybersecurity.

My curiosity about cybersecurity has led me to delve deeper into concepts of protection, process analysis, and technical automation, always with a practical and results-oriented approach. I am currently developing my own projects, which allow me to stay up-to-date with the rapid pace of technological advancements and provide higher quality instruction in my classes.

I have also competed in technical events: I won a silver medal at Andalucía Skills 2025 in the specialty of Network Systems Administration, which demonstrates my technical ability and commitment to practical excellence.

📚 My way of teaching
I like to teach in a clear, practical, and approachable way: nothing excites me more than seeing someone go from thinking "I don't even know where to start" to "I can do this myself." My classes are designed so that each concept has real-world applications from the very first minute, with exercises you can use in your own projects.

This is what I offer in my programming and technology classes:
Step-by-step explanations, without unnecessary jargon
Real code, not just theory
Exercises applied to practical situations
Close support and answers to your questions right away.
A pressure-free environment where you can learn at your own pace.

🚀 What skills should I bring to your classes?
✔ Connection between theory and real projects.
✔ Continuous and real learning.
✔ Ability to think like a computer scientist.
✔ Advanced knowledge and personal experience.

💡 My promise
I'm not just going to teach you "what something does."
I'm going to teach you how a developer thinks, how to structure code professionally, and how to solve real-world problems using programming.

If you really want to learn, from someone who has practiced and applied what they teach, I'm here to help you.
Education
2024 - Present / IES MARTÍNEZ MONTAÑES - SEVILLE
Higher Level Vocational Training Cycle in Web Application Development.

2024 - 2025 / IES MARTÍNEZ MONTAÑES - SEVILLE
Preparation for the Andalucía Skills 2025 competition, in the 39 IT Network Systems Administration modality.

2022 - 2024 / IES MARTÍNEZ MONTAÑES - SEVILLE
Medium Level Training Cycle in Microcomputer Systems and Networks.

2018 - 2022 / IES CARMEN LAFFÓN - SEVILLE
Compulsory Secondary Education.

Licenses and certifications:
- Runner-up in Andalucía Skills 2025 - 39 IT Network Systems Administration.
- Santander Open Academy course - Copilot (8 hours).
- Santander Open Academy Course - Responsible Prompting (8 hours).
- Santander Open Academy Course - AI and Productivity (2 hours).
- OpenWebinars Course - Web Hacking (6 hours).
- OpenWebinars course - Iptables (10 hours).
- OpenWebinars course - OneDrive (2 hours).
- OpenWebinars course - Vagrant (8 hours).
Experience / Qualifications
Full Stack Web Developer Intern
TORSA GLOBAL – SEVILLE · February 2026 – Current
Development of internal web applications with PHP, JavaScript and Bootstrap under MVC architecture.
Full Stack development with integration of visualization libraries such as eCharts and SweetAlert.

Director of the Area of Technological Development, Social Networks and Artificial Intelligence
INMEXTIA CONSULTANTS – SEVILLE · April 2025 – February 2026
Management and supervision of the company's technological area.
Development of websites and internal management web applications.
Implementation of artificial intelligence-based solutions.
Digital infrastructure management and systems optimization.
Strategic management of social media presence and digital transformation.

Software Developer Intern
EUSA University Centre – SEVILLE · February 2025
Development of custom web applications and tools.
Integration of AI models for automation and data analysis.
Support in systems administration and maintenance of development environments.
Participation in technological innovation projects and software design.

Store Assistant
Telepizza – SEVILLE · September 2024 – December 2024
Customer service and order management.
Cashier duties and inventory control.
Kitchen support and product restocking.
Maintaining order and operational efficiency.

Systems Administrator Intern
EUSA University Centre – SEVILLE · March 2024 – June 2024
Installation, configuration and maintenance of hardware and software.
Administration of local networks, servers and computer equipment.
Diagnosis and resolution of technical incidents.
Support in system management and security tasks.

ATM
La Ponderosa – MALAGA · August 2021
Collection and direct customer service at the fairgrounds.
Cash management and ticket control.
Coordination with the team to optimize the sales flow.
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Duration
30 minutes
45 minutes
60 minutes
The class is taught in
Spanish
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
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-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

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A 34-year-old university graduate, business engineer, with 12 years of professional experience, offers her teaching services.

Lessons (1 hour online only) - 1.5 or 2 hours depending on the subject/level/location
Educational and organized teacher with excellent revision and organizational techniques
Microsoft Office - Excel/Word/PowerPoint - email - typing - ...

Educational and patient with a good methodology adapted to each student

97% success rate with regular monitoring

Feel free to contact me for more information!
See you soon
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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
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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.
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This computer science support course is designed for students and learners wishing to strengthen their foundations or improve their level in computer science and programming.
I support participants in a pedagogical and progressive manner, adapting to their level and objectives (university courses, training, practical work, exams, projects).
The goal is to understand, practice and gain autonomy through clear explanations and concrete examples.
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Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
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The terminal isn't scary, it's your superpower. Learn Linux the practical way and start working like a pro.

I work with Linux daily, running servers, analyzing logs, automating tasks with scripts, and securing systems for government, financial, and telecom organizations. I'll teach you the commands and habits that actually get used on the job. No boring theory dumps, just live, hands-on practice.

You'll learn:

Linux basics: the file system, users, and how everything fits together
Essential commands for navigating, creating, copying, searching, and editing files
Permissions and ownership: chmod, chown, sudo, and staying safe as root
Processes, services, and package management (apt, systemctl, and more)
Text processing and log analysis with grep, awk, sed, and pipes
Networking commands and remote access with SSH
Bash scripting basics to automate your everyday tasks

Perfect for: beginners, students, career changers, developers, IT staff, and anyone preparing to learn cybersecurity, DevOps, or system administration.

You'll walk away with the confidence to work in any Linux terminal, the ability to automate repetitive tasks, and a solid foundation for IT, cybersecurity, or development careers.

No experience needed, just curiosity and a computer. Book your first session and let's open the terminal!
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Session 1: Revolutionizing your Scientific Writing with LaTeX & AI
Duration: 2 Hours | Level: Beginner | Tools: Overleaf + AI**

First Hour: Foundations and Cloud Environment (60 min)

1. Introduction to LaTeX Philosophy (15 min)

- The "WYSIWYM" concept:** Explain the difference between Word (*What You See Is What You Get*) and LaTeX (*What You See Is What You Mean*). Why content takes precedence over form.
- Key advantages:** Unrivaled typographic quality, automatic reference management, stability on long documents (theses), and free of charge.
- The structure of a file:** Distinction between the **preamble** (the brain: settings and packages) and the **body of the document** (the heart: text).

2. Immersion in Overleaf (25 min)

- Configuration:** Creation of an account and first project "Blank Project".
- Exploring the interface:** The file panel (left), the code editor (middle) and the PDF preview (right).
- Real-time collaboration:** How to share a project and leave comments (like on Google Docs).
- History and versions:** How to revert to a previous version in case of a compilation error.

3. Practical Workshop: My First Document (20 min)

* Writing basic commands: `\documentclass`, `\usepackage[french]{babel}`, `\title`, `\author`.
* Compilation of the document and observation of the result.
* Structuring: Use of `\section` and `\subsection`.

Second Hour: Mathematics and the Magic of AI (60 min)

4. The Power of Mathematics (20 min)

- Mathematical modes:** Difference between the text (`$...$`) and the centered block (`\[...\]`).
- Essential syntax:** Fractions `\frac{}{}`, exponents `^`, indices `_`, and roots `\sqrt{}`.
- Introduction to AMS packages: Why amsmath and amssymb are essential for professional rendering.

5. From hand to screen: AI at the service of LaTeX (30 min)

- Presentation of OCR tools:** Use of **Mathpix Snip** (the leader) or models like Gemini/ChatGPT to transform a photo into code.
- Concrete demonstration:
1. Take a picture of a complex handwritten formula (e.g., an integral with matrices).
2. Use AI to generate the corresponding LaTeX code.
3. Correction and insertion: Learn to check the AI-generated code before copying and pasting it into Overleaf.

6. Conclusion and Q&A (10 min)

* Summary of achievements.
* Resources for further exploration
* Definition of the exercise for the next session.
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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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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.
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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.
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Additional materials + practice qs | Lesson plans + regular feedback | Recording + session notes available

A common theme running through our lessons will be to simplify - even topics like electromagnetic induction can be reduced to small bits :)

Together, we'll:
1. Find learning gaps
2. Break concepts into small bits
3. Apply to real world and exam questions
4. Work on exam technique - like the difference between "explain" and "describe" questions
5. Build mark-scheme friendly language

“It's not that I'm so smart, it's just that I stay with problems longer”
- Albert Einstein, while studying superposition of waves, I’m sure :)
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