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Instructor since March 2022
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React.js – Basics + Easy Mini Project for Beginners
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From 16 £ /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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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
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• 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

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• 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

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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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• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
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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

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
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• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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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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Digital electronics
Analog electronic
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C / c ++ programming, Assembler, ARM, STM32
renewable energy (wind, PV)
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Resumption and deepening of fundamental concepts through exercises with course reminders.

Put the student in a situation of questioning and research.

Respond to individual issues and questions

Exercise training in order to achieve real mastery of the content.

Learn to build theoretical reasoning from observable facts or hypotheses.

Specific preparation for higher education requirements (in-depth content, increase in work capacity, enrichment of scientific background)

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Associate professor provides support courses in electrical engineering
verified badge
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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.
verified badge
Give your child the future he deserves.

these courses are intended to help your child succeed in significantly increasing the average by assimilating the course and preparing for the next tests (assimilating the course, practicing standard exercises, anticipating the teacher's expectations and test questions, have writing methods) in the following subjects:
Maths - Physics-Chemistry and Computer Science.
verified badge
Don't settle for anything less than excellence.
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With over 12 years of tutoring experience, I have successfully guided more than 50 students worldwide to achieve distinction in various fields. Consistent results are my priority, and I strive for excellence in all aspects of my teaching.

My lessons are customized to meet each student's unique needs and are designed to be engaging and insightful.

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- Preparation for IB/IA, A-Levels, GCSE, University Entry, or equivalent.
- Experience in preparing students to access world-class schools and universities, including Cambridge University, Oxford, Ivy League and other top institutions in the UK and US.
- University levels (undergraduate and postgraduate).
- High school studies and diploma programs.
- Assistance with specific projects at a professional level, including job interview preparation.
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Every lesson is meticulously planned in advance to ensure that it aligns with your goals and targets areas for improvement.
I prioritize a dynamic and interactive learning experience, with one-on-one sessions tailored to your individual requirements. Lessons will be conducted via webcam, enabling you to connect from anywhere.

I have a highly flexible schedule and can adapt to accommodate your needs.
If you have any questions about my teaching method, availability, or pricing, please don't hesitate to reach out. I am here to assist you and provide the support you need.
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Work with data or images?
Automate repetitive tasks?
Control or manage your own hardware?

Whether you are just starting to learn Python or already have a specific project and need some guidance, I would be happy to help you.

My goal is to explain things clearly, adapt to your level, and help you understand not only how to make something work, but also why it works.

Let's turn your ideas into working Python projects!
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Master Python with Personalized Courses

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.

Why Choose My Courses?

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

Practical Experience: Learn by doing with real-world projects that build your understanding and skills.

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.

Book Your First Lesson:

Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.
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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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💻 Computer skills: an essential 21st-century skill

Today, mastering computer skills is no longer a luxury, it is a necessity.
Not having the basics can quickly become a hindrance to school, academics or career.

👉 Contact me now to:

• acquire a solid foundation in computer science
• deepen the fundamentals of programming (for advanced levels)
• Strengthen your skills in a clear, structured and sustainable way

📘 Mathematics – Secondary Level
Are you experiencing difficulties? I will guide you step by step to raise your level, understand and progress methodically.

📍 Lessons at home or remotely (video conference)
🎯 Objective: to understand, reason, and succeed
📞 Available for customized support.
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Python is today one of the most widely used programming languages in the world, both in Data Science, Artificial Intelligence, Web Development and for task automation.
In this course, I will guide you step by step according to your level:

Beginner: basics of the language (variables, loops, conditions, functions).

Intermediate: data manipulation (Pandas, NumPy), file management, object-oriented programming.

Advanced: practical projects (data analysis, machine learning, automation, API, web scraping).

My goal is to make learning clear, practical, and motivating. You'll not only learn how to code in Python, but also how to structure your projects and apply your knowledge to real-life scenarios.
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As a Digital Transformation student, I know that programming is a fundamental building skill—whether you are a future engineer or a curious young learner. I designed this course as a practical guide to mastering essential tools and, above all, to developing the creative mindset of a programmer.

You will learn how to break down complex problems into logical steps, turn your ideas into functional code, and view errors (“bugs”) as stimulating challenges rather than obstacles.
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*Goal: To understand artificial intelligence without fear, use it to simplify life, and uncover digital traps.*

### **1: Demystifying Artificial Intelligence (What Exactly Is It?)**

* **Artificial intelligence is not a "movie robot":** The fundamental difference between science fiction and practical reality.
* **How it works (simply):** Imagine a "giant library"; AI has read billions of books and uses them to predict the completion of a sentence or create a new image.
* **Where do we use it now?** The spell checker, Netflix and YouTube suggestions, GPS navigation, and voice assistants such as Siri and Alexa.

---

### **2: Using artificial intelligence to make your life easier**

* **Interacting with artificial intelligence (ChatGPT, Cloud, Gemini):**
* Writing formal emails or complex letters.
* Summarizing long articles or huge documents.
* Plan travel itineraries or create food recipes from ingredients available in the refrigerator.


* **Creativity and Memory:**
* Create creative images for greeting cards (via Midjourney or DALL-E).
Restoring and coloring old family photos.

3: The art of speaking to the machine (the Prompt skill)**

* **Context Style:** Why is the phrase "Give me a cake recipe" less effective than "I have a gluten allergy and I'm having 4 people, give me a simple chocolate cake recipe".
* **Role-taking:** Learn to ask the artificial intelligence to "speak like a tourism expert" or "answer me like a specialized agricultural engineer."

4: Precautions and Critical Thinking (A Survival Guide)**

Digital hallucinations:** Understand that artificial intelligence may confidently present false information (never rely on it for medical or legal advice without verification).
Privacy protection
Do not share sensitive data (ID numbers, passwords, bank details).
Be aware that everything you write may be used to train systems in the future.

Detecting deepfakes:**
How to identify fake photos or videos (examine hand details, strange reflections, or metallic sound).
* The golden rule: Verify by cross-referencing different sources.

5: Ethics and Impact (A Future Perspective)**

Copyright: Who owns the image created by artificial intelligence?
Environmental impact: Water and energy consumption in large data centers.
The future: Will artificial intelligence replace us or will it be an assistant to us?

Additional tip:** Since you are targeting the Gulf region, it is preferable to use terms such as "Digital Transformation" and "Innovation" at the beginning of your presentation, as they are very catchy words for decision-makers there.
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These courses are part of a structured and progressive training in Object-Oriented Programming (OOP) with JavaScript, designed for beginner or intermediate developers who want to understand in depth how the language works, write clearer, more maintainable code and prepare themselves calmly for modern frameworks like React ⚛️.

Object-Oriented Programming is often perceived as complex or abstract.

My goal is simple: to make it logical, concrete, and immediately applicable.

🎯 Training Objectives

Upon completion of this training, you will be able to:

Understanding what Object-Oriented Programming really is (and when to use it)
Create and manipulate objects in JavaScript in a clean and efficient way
Use ES6 classes, constructors, and methods with confidence
Mastering this, the prototype, and the instantiation logic
Apply encapsulation, inheritance, and polymorphism without confusion
Avoiding common mistakes made by OOP beginners
Structure your JavaScript code like a professional developer

📖 Training Plan – Object-Oriented Programming in JavaScript
1. Introduction to Object-Oriented Programming 🧠
Understanding the concept, objectives and benefits of OOP.
2. Procedural Programming vs. OOP
Why unstructured code quickly becomes unmanageable.
3. Objects in JavaScript
Properties, methods and representation of the real world.
4. The keyword this
Understanding the execution context (often poorly understood).
5. Limitations of simple objects
Why duplicating code is a bad idea.
6. Constructive functions
Create multiple objects from the same model.
7. The keyword new
What it's actually doing under the hood.
8. The prototype
Sharing methods and memory optimization.
9. ES6 Classes
Modern syntax and best practices.
10. The builder
Proper initialization of objects.
11. Data Encapsulation
Protect the internal state of objects.
12. Inheritance between classes
Reusing code intelligently.
13. The keyword super
Communication between parent and child in the classroom.
14. Polymorphism
The same behavior, several forms.
15. Composition vs. Inheritance
Choosing the right architecture.
16. Best practices in OOP
Write readable, scalable, and maintainable code.
17. Common mistakes made by beginners
Pitfalls to absolutely avoid.
18. Guided practical exercise
Creation of a concrete class (product, user, etc.).
19. Assessment Quiz (Multiple Choice Questions)
To validate the actual understanding of the concepts.

🛠️ Teaching method: Understand before writing

This training program is based on a progressive and pragmatic approach:
Clear and illustrated explanations
Concrete examples from real projects
Simple but effective exercises
Constant questioning to avoid rote learning
Adaptation to the learner's level and pace
Here, we don't "recite OOP" — we understand it.

🚀 Learner's result

At the end of the training, you will not only know how to write a JavaScript class.
You will know:

1- Why does it exist?
2- When to use it
3- and when not to use it

You will leave with:
a solid understanding of OOP
a cleaner and more professional code
an ideal foundation for learning React, Node.js or any other modern framework
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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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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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