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Since March 2022
Instructor since March 2022
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React.js – Basics + Easy Mini Project for Beginners
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From 16.03 £ /h
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This course is for beginners and apprentices who want to learn React.js and create their first mini web project.

You will learn :

Installing and configuring a React environment

Basic concepts: components, accessories, condition

Event and form management

Using Hooks (useState, useEffect)

Simple connection to an API or local storage

Completion of a mini practical project (e.g., To-Do List, Portfolio, Mini Dashboard)
Extra information
Laptop required

Install Node.js and VS Code before the course

No prerequisites for beginners, just motivation!
Location
location type icon
Online from Morocco
About Me
I am a full stack web and mobile developer and hold a Master’s degree in ISI (Information Systems Engineering). I have extensive experience in designing, developing, and deploying web and mobile applications, and I teach students how to build real-world projects from scratch.

Skills and Competencies:

Front-End Development:

HTML5, CSS3, JavaScript, TypeScript

Responsive Web Design, Bootstrap, Tailwind CSS

Frameworks: Angular, React.js, Vue.js

UI/UX design principles, interactive web interfaces, single-page applications (SPA)

Back-End Development:

PHP programming, Symfony framework

Node.js & Express.js

RESTful API development and integration

Database management: MySQL, PostgreSQL, Firebase

Mobile Development:

Flutter (front-end), Dart programming

Mobile apps connected to APIs and databases

Firebase integration (authentication, storage, real-time data)

Additional Skills:

Git/GitHub for version control

Deployment: Heroku, Firebase Hosting, cPanel

Debugging, optimization, and error correction

Project architecture (MVC, modular code)

Agile methodology and project management basics

What I offer:
I guide students step by step, provide personalized advice, mentorship, and code review. I help apprentices and beginners complete their web or mobile projects successfully.
Education
Master’s Degree in ISI (Information Systems Engineering), Faculty of Sciences, Qadi Ayyad

Professional Bachelor’s Degree in Web Technology and Programming, Faculty of Sciences, Qadi Ayyad

DEUG in Mathematical and Computer Sciences, Faculty of Sciences, Qadi Ayyad
Experience / Qualifications
Full Stack Mobile Develope

Full Stack Web Developer

Experience in building real-world applications for apprentices, students, and beginners

Mentoring and supporting students in projects, debugging, and best practices
Age
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
French
English
Arabic
Reviews
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
This programming course is intended for beginners as well as people who already have a foundation and wish to improve their skills.
We will cover programming logic, the basics of algorithms and practice through concrete examples.

Depending on your level and objectives, the course may include:

Introduction to programming

HTML, CSS, JavaScript

PHP / Node.js

Databases (MySQL, PostgreSQL)

Understanding and correcting errors in the code

Completion of small practical projects

The approach is progressive, clear, and practice-oriented.
Read more
Personalized support for students, freelancers and entrepreneurs wishing to succeed in their IT projects.

Help with:

Project structuring

Technology choices

Specifications

Code correction and improvement

University projects (final year projects, final year projects, dissertations)

Finalization and delivery of the project
Read more
Show more
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Using AI to restore or colorize old family photos.

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

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

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

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

Using AI to restore or colorize old family photos.

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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".

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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.

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

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Copyright: Who owns an image created by AI?

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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
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Artificial Intelligence and programming become much easier when you understand the reasoning behind the algorithms—not simply memorize Python syntax or use AI tools as a black box.

I am a PhD-qualified engineer, university professor, researcher, programmer, and multidisciplinary tutor with more than 30 years of experience across teaching, technical training, engineering, information systems, quantitative analysis, research, data mining, programming, and intelligent knowledge-based systems.

This class provides a structured and personalized pathway for beginners, school and university students, researchers, engineers, professionals, career changers, and adult learners. Depending on your goals, we can focus on Python programming, computational problem solving, automation, machine learning, artificial intelligence, or a coherent progression connecting them.

PYTHON PROGRAMMING & COMPUTATIONAL THINKING
• Python installation and development environments
• Variables, data types, operators, and expressions
• Input, output, and program flow
• Conditional statements and decision making
• For loops and while loops
• Functions, parameters, return values, and scope
• Strings and text processing
• Lists, tuples, sets, and dictionaries
• File handling and data input/output
• Error handling and exceptions
• Modules, packages, and reusable code
• Object-oriented programming
• Algorithms and computational problem solving
• Debugging and systematic error correction
• Code organization, readability, and good programming practices

SCIENTIFIC COMPUTING, DATA & AUTOMATION
• NumPy for numerical computing
• pandas for structured data manipulation
• matplotlib for visualization
• Scientific and engineering calculations
• Automation of repetitive tasks
• Data processing workflows
• Working with files and external data
• Introduction to APIs when relevant
• Python and SQL workflows
• Research and quantitative applications
• Project development from idea to working solution

MACHINE LEARNING
• Foundations of machine learning
• Supervised and unsupervised learning
• Regression and classification
• Clustering and pattern discovery
• Decision trees and rule-based approaches
• Feature selection and data preparation
• Training, validation, and testing
• Model evaluation and performance metrics
• Overfitting and underfitting
• Bias, variance, and generalization
• Model comparison and interpretation
• Predictive modelling and data mining
• Neural-network foundations

ARTIFICIAL INTELLIGENCE & INTELLIGENT SYSTEMS
• Foundations and major branches of Artificial Intelligence
• How intelligent systems represent, classify, predict, and support decisions
• Knowledge representation concepts
• Ontologies and structured knowledge
• Rule-based reasoning and expert-system foundations
• Intelligent decision-support systems
• Generative AI and large language model concepts
• Prompt design and effective AI-assisted workflows
• AI limitations and hallucinations
• Bias, privacy, ethics, and responsible AI
• Applications in engineering, research, business, education, and professional decision making

Depending on your goals, practical work may involve Python, NumPy, pandas, matplotlib, relevant machine-learning libraries, Weka, SPSS Modeler, structured datasets, or modern generative-AI tools.

My teaching approach follows a clear progression:
understand the problem → design the logic → represent and prepare the data → write or select the method → test it → evaluate the output → debug or improve it → interpret the result → apply it responsibly

I do not simply provide finished code, demonstrate isolated commands, or recommend an AI model because it is popular. I help you understand why a method works, what assumptions it makes, when it should be used, how to evaluate its results, where it may fail, and how to improve the solution.

We can work with your course syllabus, programming exercises, existing code, error messages, dataset, research problem, AI project, automation task, engineering application, model output, or professional use case.

Whether you are writing your first Python program, preparing for a university course, debugging a project, automating a professional task, learning machine learning, or exploring advanced AI applications, I will adapt the sessions to your level, objectives, and pace.

My goal is to help you become an independent computational problem solver who can understand, build, evaluate, and apply intelligent solutions—not merely copy code or use AI tools without understanding them.
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