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Since May 2023
Instructor since May 2023
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Course on Digital Circuits: FPGA, VHDL, SYSTEMVERILOG, UVM.
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From 17.78 € /h
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If you're seeking to gain a competitive edge in the VLSI field, you have found the ideal resource. As a seasoned Hardware Design Verification Engineer, I offer my tutoring services to individuals interested in delving into Digital Electronics, as well as FPGA/ASIC/SoC Circuits. Whether you require assistance in Design (VHDL, VERILOG, SYSTEMVERILOG) or Verification (Simulation Synopsys), I am well-equipped to provide the support you need. Please feel free to reach out to me without hesitation.
Location
location type icon
Online from Morocco
About Me
I am a hardware design verification engineer and a PhD student in deep learning and embedded systems, if you are looking for courses on:
digital electronics, design and verification processes, artificial intelligence, machine learning and deep learning, then you are in the right place.

Skills:
• Artificial intelligence, machine learning and deep learning
• Verification methodologies: UVM, C-driven, formal (SVA), OOP, constrained random verification, metric-driven verification, UVM and C based testbenches, code coverage.
• Languages: System Verilog, C, C++, Tcl, Verilog, VHDL, Makefile, Python, XML.
• Simulation: VCS/Synopsys.
• Data management, tracking: SVN, JIRA.
• Flow: verification plan, schedules, VIP, test benches, sequences and test cases, integration tests, coverage, checkers and assertions, regressions.
Education
Doctoral student: Development/optimization of embedded systems and image processing algorithms for the detection of traffic offenses in real time.
Engineer in embedded electronic systems.
Experience / Qualifications
2+ years as a verification engineer.
1 year+ as an automotive software engineer.
Skills:
• Artificial intelligence, machine learning and deep learning
• Verification methodologies: UVM, C-driven, formal (SVA), OOP, constrained random verification, metric-driven verification, UVM and C based testbenches, code coverage.
• Languages: System Verilog, C, C++, Tcl, Verilog, VHDL, Makefile, Python, XML.
• Simulation: VCS/Synopsys.
• Data management, tracking: SVN, JIRA.
• Flow: verification plan, schedules, VIP, test benches, sequences and test cases, integration tests, coverage, checkers and assertions, regressions.
Age
Preschool children (4-6 years old)
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
English
French
Arabic
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
The aim of this program is to provide you with the necessary skills and experience to begin your journey and get a head-start in Machine Learning.
Covering the primary types of machine learning, the program offers a comprehensive theoretical understanding of Machine Learning with opportunities to practice using algorithms, methods,
and best practices associated with Machine Learning. You will also have the chance to develop your own projects using relevant open-source frameworks and
libraries and apply your learnings in various courses to a final project.

Whether you are already proficient in Python programming, statistics, and linear algebra, or have a general interest and are willing to learn,
this beginner/intermediate oriented series is suitable for you.
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My difference: you send your materials first, I confirm fit, then we use a repeatable system that builds results fast.

FAST START (SEND MATERIALS FIRST)
---------------------------------------------------
Send your syllabus or PDF slides, past papers, tutorial sheets, or assignment brief.

I confirm fit before we book. If it is outside my strengths, I will tell you directly.

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---------------------------------------------------
• Thermodynamics
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---------------------------------------------------
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• speed, structure, presentation

AFTER EACH LESSON (WHAT YOU RECEIVE)
---------------------------------------------------
• short revision note, key method + mistakes to avoid
• focused practice list
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MATLAB / SIMULINK SUPPORT (WHEN NEEDED)
---------------------------------------------------
• modelling, simulation, plotting, debugging
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COURSEWORK + ASSIGNMENTS (LEGIT SUPPORT ONLY)
---------------------------------------------------
• understand the brief, plan your approach, improve your method
• review your work, fix errors, strengthen reasoning

No done-for-you submissions, no cheating.

MESSAGE ME LIKE THIS (FAST REPLY)
---------------------------------------------------
1) Level:
2) Module/Course name:
3) Exam date or deadline:
4) Topics:
===================================
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For many years, I have successfully supported Swiss students in the fields of statistics, data analytics, machine learning, and artificial intelligence. I have gained extensive experience using R as a statistical programming tool and know exactly what students in Switzerland are required to do. I have numerous sample projects, datasets, and exam questions at my disposal and have prepared many students for their assignments and exams in a targeted, sustainable, and highly successful manner. My focus is on explaining complex statistical procedures, algorithms, and data analyses in an understandable way, demonstrating them in a practical way, and building confidence in the secure handling of data. My goal is not only to improve grades, but also, in the long term, to develop a deep understanding of data-driven questions and modern technologies such as machine learning and AI, which are crucial in later professional life.

► How do I teach?

►I attach great importance to ensuring that my students truly understand statistical concepts, data analytics methods, machine learning, and AI models, rather than just applying them mechanically – this is how we achieve sustainable success in statistics, data analytics, and modern technologies.

►My success is based on my ability to flexibly adapt my teaching style to the individual needs of students in statistics, data analytics, machine learning and AI, including topics such as regression methods, logistic regression and other machine learning algorithms.

►I use practical, lively examples from statistics, data science and AI to clearly explain abstract concepts such as hypothesis testing, analysis of variance, linear and nonlinear models and to anchor them deeply in the memory.

►With patience and clarity, I break down complex statistical analyses, machine learning models, and data processes into their essential components so that no student is overwhelmed and can build a solid foundation in statistics, data analytics, and AI.

►I am convinced that trusting collaboration is crucial to reducing uncertainty in statistics or data science and creating a productive learning atmosphere.

►I offer intensive exam preparation and project support in statistics, data analytics, machine learning and AI, develop strategies to combat exam anxiety with my students and train them on real data sets and complex algorithms.

►As an experienced online tutor for statistics, data analytics, machine learning, and AI, I use modern tools such as interactive whiteboards and live coding sessions in R to make teaching as efficient and practice-oriented as possible.

►Locations: I teach statistics, data analytics, machine learning, and AI at your home, online, or by appointment – flexible, professional, and tailored precisely to your needs.
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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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This online programming class is designed for students who want to build real technical skills in computer science and web development — not just learn theory, but actually create and understand how systems work.

I teach:

• Python programming (fundamentals, logic building, problem-solving, data handling)
• JavaScript (core concepts, DOM manipulation, interactivity)
• CSS (styling, layout systems, responsive design)
• Website Development (front-end foundations and structure)
• WordPress Development (customization, site building, content management)
• UI/UX Fundamentals (design thinking, layout clarity, user experience principles)

Students learn how to think computationally, break down problems logically, and build structured solutions. Lessons are practical and project-based, meaning students don’t just watch — they build.

For beginners, we focus on strong foundations: understanding how code works, writing clean syntax, and developing confidence in debugging.
For intermediate learners, we move into structured projects such as creating simple websites, improving layout design, adding interactivity, and understanding how front-end components connect.

My approach emphasizes clarity, structure, and application. I help students understand not only what to type, but why it works — which is the difference between copying code and truly understanding it.

This class is suitable for:
• School students exploring computer science
• Beginners transitioning into tech
• Learners preparing for academic computer science courses
• Individuals interested in web development skills

All sessions are fully online, interactive, and tailored to the student’s pace and goals.
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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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If you want clear, exam-ready Engineering and Mechanical Engineering help, this is a strong fit.

My difference: you send your materials first, I confirm fit, then we use a repeatable system that builds results fast.

FAST START (SEND MATERIALS FIRST)
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Send your syllabus or PDF slides, past papers, tutorial sheets, or assignment brief.

I confirm fit before we book. If it is outside my strengths, I will tell you directly.

WHAT I HELP WITH (HIGH-INTENT TOPICS)
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• Thermodynamics
• Solid Mechanics / Strength of Materials
• Engineering Dynamics
• Control Systems
• Engineering Materials
• MATLAB + Simulink (modelling, simulation, verification)

HOW EACH LESSON WORKS (TYPICAL SESSION)
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1) Diagnose
• identify the exact weak point, concept, method, or exam technique

2) Build (clear and fast)
• definitions, assumptions, units, sign conventions
• diagrams where needed (FBD, shear and moment, block diagrams)

3) Solve (guided)
• I show the method on a representative question
• you solve a similar one with live correction
• quick checks, units, limits, sanity

4) Exam mode (when relevant)
• past papers, mark schemes, what earns marks
• speed, structure, presentation

AFTER EACH LESSON (WHAT YOU RECEIVE)
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• short revision note, key method + mistakes to avoid
• focused practice list
• progress note, strengths, gaps, next targets

Optional: recorded session for revision

MATLAB / SIMULINK SUPPORT (WHEN NEEDED)
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• modelling, simulation, plotting, debugging
• verify results against theory, interpret correctly

COURSEWORK + ASSIGNMENTS (LEGIT SUPPORT ONLY)
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• understand the brief, plan your approach, improve your method
• review your work, fix errors, strengthen reasoning

No done-for-you submissions, no cheating.

MESSAGE ME LIKE THIS (FAST REPLY)
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1) Level:
2) Module/Course name:
3) Exam date or deadline:
4) Topics:
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I am an experienced ESL instructor with over 10 years of proven results working with learners across all levels, from beginners to advanced professionals. I specialize in assessment preparation, including standardized exams, and have a strong track record of helping students achieve measurable score improvements. My technical background allows me to deliver clear, structured instruction, particularly suited to analytical and goal-oriented learners. I focus on efficient, results-driven lessons tailored to each student’s academic, professional, or certification goals.
Good-fit Instructor Guarantee
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