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This teacher has a fast response time and rate, demonstrating a high quality of service to their students.
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Since June 2022
Instructor since June 2022
Emergency Project Help: Debug, Fix & Deploy Your Web Application Fast
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From 26 € /h
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Your project is broken? Deadline approaching? Can't deploy? I help developers and students fix bugs, optimize code, and deploy applications to production.
I Specialize In:

Emergency Debugging: Find and fix errors fast (frontend crashes, backend timeouts, database issues)
Deployment Rescue: Get your app live when nothing works (AWS, Vercel, Netlify)
Performance Optimization: Speed up slow applications (database queries, API responses, bundle size)
CI/CD Setup: Automate your deployment pipeline (GitHub Actions, testing, monitoring)

Common Problems I Solve:

❌ "My app works locally but crashes in production"
❌ "Database queries are too slow"
❌ "Authentication isn't working"
❌ "Can't deploy to AWS / Vercel"
❌ "Getting weird errors I don't understand"
❌ "Payment integration (Stripe) not working"

Technologies I Work With:

Frontend: React, Next.js, TypeScript, Vue, Angular
Backend: Node.js, NestJS, Express, Python (Django, Flask)
Databases: PostgreSQL, MySQL, MongoDB, Redis
Cloud: AWS (EC2, RDS, S3), Vercel, Netlify, Render
DevOps: Docker, CI/CD, GitHub Actions, Nginx

Perfect For:

Students: Fix your project before the deadline
Junior Developers: Debug production issues you can't solve alone
Freelancers: Get unstuck on client projects fast
Startups: Fix and deploy your MVP without hiring a full-time engineer

How It Works:

Live Debugging Session: We fix it together via screen share
Code Review: I show you how to prevent the issue in the future
Documentation: You get a summary of what was fixed and why

Average Resolution Time:

Simple bugs: 1-2 hours
Deployment issues: 2-3 hours
Complex debugging: 3-5 hours

Urgent projects accepted (same-day availability for emergencies).
Location
location type icon
Online from Netherlands
About Me
I'm Oussama, a Full-Stack Engineer with 3+ years of professional experience building production applications for international clients.

Professional Background:
12+ projects delivered on-time including SaaS platforms, e-commerce sites, and custom dashboards
Reduced deployment time by 88% at DEF TUNISIE through CI/CD automation
Active freelancer serving clients across Europe and North America
Former instructor at Masterclass Denden, where I taught 50+ students (100% project completion rate)

What Makes My Teaching Different:
You learn from real production code, not tutorials
I show you how companies actually build software (debugging, deployment, monitoring)
Focus on practical skills that get you hired or help you ship your project

Technologies I Work With:
Frontend: React, Next.js, TypeScript, Tailwind CSS
Backend: Node.js, NestJS, Express, REST APIs
Databases: PostgreSQL, MongoDB, Redis
DevOps: AWS (EC2, RDS, S3), Docker, CI/CD, GitHub Actions
AI Integration: ChatGPT API, prompt engineering
Integrations: Stripe, OAuth, SendGrid

Who I Help:
Students: Ship your projects with professional standards - deployed live
Career Switchers: Build a portfolio that actually gets you hired
Junior Developers: Learn production practices bootcamps don't teach
Freelancers: Add high-value services (AI integration, DevOps) to your offerings
Entrepreneurs: Build or fix your MVP without hiring a full team
Education
Bachelor's degree in computer science and multimedia
Professional Certificate in DevOps and Software Engineering
MicroBachelors® program in Full Stack application development
Experience / Qualifications
✓ 3+ years professional software development
✓ 12+ projects delivered on-time for international clients
✓ 88% deployment time reduction at DEF TUNISIE through CI/CD automation
✓ Former Instructor at Masterclass Denden
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
French
English
Arabic
Reviews
Availability of a typical week
(GMT -05:00)
Chicago
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
Learn to build production-ready web applications from a professional engineer with 3+ years of experience delivering real client projects

What You'll Master:
Frontend: React, Next.js, TypeScript, responsive design, state management
Backend: Node.js, NestJS, REST APIs, authentication (JWT, OAuth), security best practices
Databases: PostgreSQL, MongoDB - proper schema design, queries, optimization
DevOps: Deploy to AWS (EC2, RDS, S3), Docker, CI/CD with GitHub Actions
Integrations: Stripe payments, email automation, file uploads

What Makes This Different:
Work on YOUR project (PFE, portfolio, startup MVP) - not generic tutorials
Learn production debugging - how to fix real errors, not just write code
Get your app deployed LIVE with proper CI/CD and monitoring
GitHub repo ready to show employers with professional commit history

Perfect For:
Students: Ship your projects with professional standards (authentication, deployment, documentation)
Career Switchers: Build 2-3 portfolio projects that get you hired
Junior Devs: Learn what companies actually need (debugging, testing, deployment automation)

What You'll Build:
Full-stack application deployed to AWS
User authentication system (login, OAuth, password reset)
Admin dashboard with real-time data
Payment integration with Stripe
Complete with CI/CD pipeline and monitoring

Bonus: I'll review your resume and help position you for remote developer roles.
Read more
Learn to add AI features to your applications using ChatGPT API. Taught by a Full-Stack Engineer who's integrated AI into real client projects.

What You'll Learn:
ChatGPT API Integration: Add AI chat, content generation, or text analysis to any web app
Prompt Engineering: Write effective prompts that give consistent, quality results
Production Implementation: Rate limiting, cost management, error handling, security
Real-World Deployment: Deploy AI features to AWS with proper monitoring and logging

Technologies:
OpenAI API (ChatGPT-4)
React/Next.js frontend integration
Node.js/NestJS backend
Secure API key management
Cost optimization (don't waste money on API calls)

Perfect For:
Developers: Add AI to your portfolio (instant differentiation from other candidates)
Freelancers: Offer AI integration services
Students: Build an AI-powered projects that stands out
Entrepreneurs: Add AI features to your product without hiring an AI engineer

What We'll Build:
AI chatbot for your website (customer support, FAQ assistant)
Content generation tool (blog posts, product descriptions, social media)
Text analysis features (summarization, sentiment, keyword extraction)
Custom AI assistant for your specific use case

Real Examples:

E-commerce: AI product description generator
Real estate: AI property listing writer
Marketing: AI social media content creator
Education: AI study assistant

What's Included:
Working code you can deploy immediately
Cost management strategies (API calls can get expensive!)
Error handling and fallback strategies
Production deployment checklist

No AI experience needed - just basic JavaScript/React knowledge.
Read more
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Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

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Error handling and debugging
Problem-solving, reasoning, and programming logic
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Object-oriented programming
Practical Python exercises and projects
Introduction to Python for Artificial Intelligence and Machine Learning
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My classes are suitable for:

Complete beginners who have never programmed before
Students who want to learn Python from the basics
University and school students
People who want to improve their programming skills
Anyone who wants to learn Python for personal or professional development
Students interested in eventually moving toward Artificial Intelligence, Machine Learning, or Data Science

I'll adapt the lessons to your level, your goals, and your learning speed.

🚀 Let's learn Python together!

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My goal is simple:

Think Clearly. Solve Problems. Learn Python. Build Something.

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It's not an exam. The student tries things out. You take away a plan.

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Within 24–48 hours, a short document containing:
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• tools appropriate for their age
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I offer one-to-one Programming tuition in Python, C, and C++, for GCSE Computer Science, A-Level Computing, and university students studying engineering, computer science, or related subjects. Lessons are available online or in person around Birmingham.

What I cover:

Python for beginners and intermediate learners
C and C++ programming
GCSE and A-Level Computer Science (all exam boards)
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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

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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 hands-on training pathway is designed to help students kickstart any project, specifically tailored for OT labs and industrial applications. Starting from absolute scratch, students will build a strong foundation in Python programming through practical, industry-relevant concepts.

Curriculum Outline: |
01 - Python Environment Setup & Basics |
02 - Python Variables, Numbers, Bytes & Hex |
03 - Control Flow Logic Functions |
04 - Data Structures (Lists, Tuples, Dictionaries & Sets) |
05 - String Formatting, Comprehensions & Exception Handling |
06 - File IO, Pathlib & Context Managers |
07 - Object-Oriented Programming (Classes & OOP) |
08 - Standard Library, Modules & Networking Basics |

Assessment & Evaluation:
Students will take a mini-test after the completion of each module. Additionally, an Audit & Performance Evaluation report will be sent following the tests.
Duration:
5 days to 15 days (depending on the pace of the cohort)
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I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples.

Topics include:
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Contact Oussama
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Most kids think coding is for "smart kids" or "future programmers."
It's not. Coding is how real people solve real problems.
In this class, we skip the theory. Your child creates real things.

What they'll do:
✓ Build real projects in Scratch: a working game, an interactive animation, a story they coded
✓ Program virtual robots: solve real-world challenges (navigate a maze, automate a task, build a system)
✓ Create in Minecraft Education: design worlds, automate constructions, solve logic problems
✓ Experiment with different languages: not just learn "the right way," but understand that there are many ways to think about a problem
✓ Collaborate and share: work with other kids, get feedback, improve their work
✓ Develop logical thinking: not just for coding, but for anything: solving math problems, science challenges, real-world situations


Why this is different:
We don't teach syntax. We teach how programmers think.
Most children's coding courses say "here's the code, copy it." We teach "what problem are we trying to solve? How could we break it into steps? What options do we have?"
When your child learns to think like a programmer, they can learn any language afterward.

What they take home:
A portfolio of 3–4 completed, working projects. The ability to say "I built this." And the deep understanding that code is a tool to make real things happen.

Format: Online or Barcelona | 60–90 min sessions | Flexible pace, no prior experience needed
For curious 8-12 year olds who want to build.
verified badge
Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

This popularity means that Python is constantly evolving. It offers a wide range of tools and libraries, which are free and very varied.

As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

Beyond these general principles, there is no magic rule or method. Some approaches work with some students but not with others. Adaptation to individual needs is therefore the main objective of private lessons. So I will do my best to find what motivates and helps my student.
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Are you interested in learning programming, Artificial Intelligence, or Machine Learning but don't know where to start? I offer personalized lessons designed for beginners, students, and professionals who want to build strong practical skills in computer science and AI.

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III) Robotics
IV) Data Science
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b) Deep Learning fundamentals
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doctoral student in engineering sciences provides support courses in analog and digital electronics at any DEUG level and engineering schools. having scientific and technical knowledge, three years of experience in the field of teaching, pedagogy and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they are having difficulty. for more info please contact me
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* This digital training aims to introduce you to the Scratch tool and through the game world, and gradually, to discover programming concepts such as loops, conditions or variables. It is aimed at anyone who is new to Scratch and who wants to create games and animations.

* Learning programming will allow students to develop their skills and will certainly allow them to meet the expectations of the future working world and emerging careers.

* In addition, learning programming allows the development of algebraic, algorithmic and computational thinking. Programming also helps to improve and develop students' sequencing ability, as well as their communication skills. Thus, there are several advantages to teaching programming, but the important thing is to remember that this learning teaches students that digital is not only for entertainment, but that it is possible to become creators. active and creative content.
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Become a Trading Pro with NinjaTrader!

Are you passionate about trading and want to master one of the most powerful trading platforms on the market? If so, this ad is for you!

As a seasoned NinjaTrader expert, I offer personalized courses to help you master this exceptional trading platform. Whether you want to learn how to trade manually or automate your strategies using C# and custom indicators, I'm here to guide you every step of the way.

My courses are designed for traders of all levels, from complete beginners to experienced professionals. Here's what you can expect:

- In-depth training on using NinjaTrader, including navigating the interface, executing orders, and analyzing the markets.
- Detailed instructions on creating and optimizing automated trading strategies using C#.
- Practical advice on using custom indicators to refine your technical analysis and make informed trading decisions.

Whether you are looking to generate additional income through trading or make trading your full-time career, my courses will provide you with the knowledge and skills needed to succeed in the financial markets.

Don't miss this opportunity to become a true trading expert with NinjaTrader! Contact me today to reserve your spot and begin your journey to financial success.
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🐍 Learn Python from Scratch — Think, Solve & Code!

Have you always wanted to learn Python programming but didn't know where to start?

Or maybe you've already started learning Python but some concepts still feel confusing?

Don't worry — you're in the right place! 😊

I'm Reza, a Computer Engineer, teacher, and technology enthusiast. I have a Master's degree in Computer Systems Architecture, I am currently studying Artificial Intelligence, and I have experience teaching programming and computer science to students with different backgrounds and skill levels.

🧠 Before Python: Learn How to Think Like a Programmer

For me, learning Python isn't just about learning commands and syntax. The most important part of programming is learning how to think when you face a problem.

Before jumping into code, we'll learn how to understand a problem, break it into smaller pieces, identify what information we have and what we need to find, and develop a step-by-step solution. Then we'll turn that solution into Python code.

We'll practice logical thinking, reasoning, problem-solving, algorithmic thinking, and debugging along the way. My goal is to help you become someone who can look at a new problem and think, "Okay, how can I solve this?" — not someone who only remembers Python syntax.

💻 What will you learn?

In my Python classes, we can start from the very beginning and gradually build your programming skills. We'll combine programming fundamentals with problem-solving and practical coding, so you understand not only how to write Python, but also how to approach a programming problem.

Depending on your level and goals, we can cover topics such as:

Python fundamentals and programming concepts
Variables and data types
Numbers, strings, and text processing
if statements and decision making
for and while loops
Lists, tuples, dictionaries, and sets
Functions and reusable code
Working with files
Error handling and debugging
Problem-solving, reasoning, and programming logic
Algorithmic thinking and step-by-step solution design
Object-oriented programming
Practical Python exercises and projects
Introduction to Python for Artificial Intelligence and Machine Learning
🎯 My teaching style

I don't want you to simply memorize Python commands.

I want you to understand how programmers think.

During our lessons, I explain concepts in simple language and then we practice them together. You'll learn how to analyze a problem, think about possible solutions, write code, test it, find mistakes, and improve your solution. You'll make mistakes, ask questions, and gradually become more confident.

I believe that learning by doing is one of the best ways to learn programming.

So instead of spending the whole lesson listening to me, you'll actually write Python code and practice what you've learned.

And don't worry if you're a complete beginner. You don't need any previous programming experience to start.

👨‍💻 Who are these Python lessons for?

My classes are suitable for:

Complete beginners who have never programmed before
Students who want to learn Python from the basics
University and school students
People who want to improve their programming skills
Anyone who wants to learn Python for personal or professional development
Students interested in eventually moving toward Artificial Intelligence, Machine Learning, or Data Science

I'll adapt the lessons to your level, your goals, and your learning speed.

🚀 Let's learn Python together!

Learning to program can seem difficult at first, but it doesn't have to be.

With the right explanation, enough practice, and a little patience, you can go from writing your first print() statement to building your own Python programs.

My goal is simple:

Think Clearly. Solve Problems. Learn Python. Build Something.

If you're ready to start learning Python, book your first lesson and let's write some code together! 🐍💻
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Description:
This course is ideal for beginners or intermediate learners who want to learn programming using languages like C#, Java, or Python. With a step-by-step approach, you'll be guided from basic algorithms to object-oriented programming.

Goals :

Introduction to algorithms and their implementation.
Master the basics of C#, Java, and Python languages.
Understand the concepts of classes, objects, and error management.
Course methods and format:

Video lessons: Clear explanations and practical exercises.
Flexibility: Personalized support to meet your expectations.
For who ?
Students or professionals starting out in programming, or preparing for exams.
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This course is a comprehensive and practical introduction to the WordPress CMS, designed for beginners who want to create, customize, and publish their own website, without writing a single line of code.

🎯 Educational objectives:

1- Understand how WordPress and its interface (back-office) work

2- Install WordPress locally using MAMP

3- Choose, install and customize a theme adapted to your needs

4- Use a builder (like Elementor) to create modern and dynamic pages

5- Create a structured navigation menu

6- Import a pre-built demo to save time

7- Put your site online and learn good maintenance practices

🧰 On the program:

1- Installing the local environment with MAMP

2- Getting started with the WordPress interface

3- Management of pages, articles, media, extensions and settings

4- Visual customization (themes, CSS, builders)

5- Hosting your website online

6- MCQs and practical exercises to validate your knowledge

💡 Who is this course for?

For students, entrepreneurs, freelancers, web enthusiasts, or anyone who wants to create their own website without relying on a developer.
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This course is designed to introduce students aged 7 to 16 to the world of programming through two of the most widely used and industry-relevant languages: C++ and Python.

The class provides a structured, age-appropriate pathway into programming, whether the student is a complete beginner or already exploring coding through platforms like Scratch or Code.org. Emphasis is placed on understanding logic, building problem-solving skills, and writing real code in a supportive, project-based environment.

Taught by an engineering student with hands-on experience in both C++ and Python, this course empowers students to explore the power of code and build a strong foundation in computational thinking — essential for future studies in engineering, robotics, AI, or game development.
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For children and teenagers who want to get started with artificial intelligence — or who already use ChatGPT and the family wants order and security.

It's not an exam. The student tries things out. You take away a plan.

What do we do during the hour?
• See what tools you already use and how you understand them
• Distinguish myths from reality (what AI is and what it is not)
• Test together some instructions/prompts and a mini-project (text, image or game idea)
• Talk about what worries you at home: copied homework, privacy, age, SEN if any.

What do you take with you afterwards
Within 24–48 hours, a short document containing:
• 3–5 AI ideas that are right for you now
• tools appropriate for their age
• How to follow: class schedule and what you can do at home

If there are SEN (ADHD, dyslexia, organization...), I adapt the session and the plan to that pace.

It serves as an entry point to regular classes. If it's a good fit, the next step is a project, not more theory.
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I offer one-to-one Programming tuition in Python, C, and C++, for GCSE Computer Science, A-Level Computing, and university students studying engineering, computer science, or related subjects. Lessons are available online or in person around Birmingham.

What I cover:

Python for beginners and intermediate learners
C and C++ programming
GCSE and A-Level Computer Science (all exam boards)
University coursework support, debugging help, and project guidance
Core concepts: variables, loops, functions, data structures, object-oriented programming, file handling, basic algorithms

How I teach:
I start by understanding exactly where you are — whether that's "I've never coded before" or "I'm stuck on a specific assignment." Then I build lessons around small, practical examples you can actually run and modify yourself. I'm patient with errors (everyone gets them), and I make sure you understand the why behind the code, not just how to copy it. For university students, I can also help with debugging, code reviews, and explaining tricky concepts in plain English.
If you or your child is preparing for exams, working on coursework, or just wants to finally feel comfortable with coding, I'd love to help.
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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 hands-on training pathway is designed to help students kickstart any project, specifically tailored for OT labs and industrial applications. Starting from absolute scratch, students will build a strong foundation in Python programming through practical, industry-relevant concepts.

Curriculum Outline: |
01 - Python Environment Setup & Basics |
02 - Python Variables, Numbers, Bytes & Hex |
03 - Control Flow Logic Functions |
04 - Data Structures (Lists, Tuples, Dictionaries & Sets) |
05 - String Formatting, Comprehensions & Exception Handling |
06 - File IO, Pathlib & Context Managers |
07 - Object-Oriented Programming (Classes & OOP) |
08 - Standard Library, Modules & Networking Basics |

Assessment & Evaluation:
Students will take a mini-test after the completion of each module. Additionally, an Audit & Performance Evaluation report will be sent following the tests.
Duration:
5 days to 15 days (depending on the pace of the cohort)
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I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples.

Topics include:
pyRevit fundamentals and setup
Automating repetitive Revit tasks (model checks, plan generation, data extraction)
Writing custom scripts for your firm's specific workflows
Applying Python automation to real MEP/BIM projects
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