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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 -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
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.
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• 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

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• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

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• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

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• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

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• 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

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• 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

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Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels.

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Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

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Ongoing Support: Get unlimited email support for any questions you have between sessions.

As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey.

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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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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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This course is designed for students and adolescents who want to build a strong beginner-friendly foundation in Computer Science.
Whether you're completely new to Computer Science or need help with a specific programming subject, the course can be customized to match your needs.
Need help with C++ or programming? You can provide me with your syllabus, course outline, or the topics you're studying, and I'll tailor the lessons around what you need to learn.
Learning with friends? Group lessons are also available, allowing you to learn together while following the same customized course.
Good-fit Instructor Guarantee
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