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Since September 2018
Instructor since September 2018
Translated by GoogleSee original
special course in Algorithms with the Python language
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From 47 C$ /h
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Student in 2nd year of the Mathematical Master passed by the preparatory classes and admitted to the common national competition then the engineering school INPT then a degree in Mathematics. I propose an individualized pedagogy, a help with the preparation of exam competitions or questions. My goal is to advance my students without overburdening them. I give assignments after each lesson and each student according to their level and I provide useful and effective preparation and revision ideas.
This tutoring course is for familiarizing the student with programming and algorithmics and then acquiring the basics of the Python language.
Extra information
bring your machine with you
Location
location type icon
Online from Morocco
About Me
I propose an individualized pedagogy, a help with the preparation of exam competitions or questions. My goal is to advance my students without overburdening them. I give assignments after each lesson and each student according to their level and I provide useful and effective preparation and revision ideas.
Education
Preparatory classes where I was among the major admitted to the CNC, engineering school INPT, Bachelor in Mathematics and Applications, student at the Master in Mathematics
Experience / Qualifications
2 years of expertise in private lessons that help me to become familiar with the teaching profession, I give assistance to the preparation of exam competitions or questions for students of the prep PSI MP TSI, students of the S1-S4 college and Baccalaureate students. My goal is to advance my students without overburdening them. I give assignments after each lesson and each student according to their level and I provide useful and effective preparation and revision ideas.
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
45 minutes
60 minutes
90 minutes
120 minutes
The class is taught in
French
Arabic
English
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
Student in 2nd year of the Mathematical Master passed through the preparatory classes for engineering schools admitted to the common national competition I did a engineering school in Telecommunication and a degree in mathematics, I propose an individualized pedagogy, an aid to the preparation of exam competitions or questions. My goal is to advance my students without overburdening them. I give assignments after each lesson and each student according to their level and I provide useful and effective preparation and revision ideas.
This Mathematics course aims to:
Answer the student's questions (lesson points, exercises and corrections given in class).
Assess the student's comprehension (direct questions, direct application exercises, quizzes, validation of the student's cards).
Verification of the work required of the student:
- Identification of any misunderstandings
- Detailed explanation of issues not included
- Correction of possible errors
- Tips resolution method and tips.
For all the exercises / annals done together, the pupil is strongly encouraged to do everything alone and to write them on their own. The student is also asked to note any difficulties encountered as well as the time spent on each exercise. This approach aims to:
- Validation of assimilation
- The assessment of the writing
- Evaluation of efficiency (speed, absence of mistakes, ...).
Read more
Student in 2nd year of the Mathematical Master passed through the preparatory classes for engineering schools admitted to the common national competition I did a engineering school in Telecommunication and a degree in mathematics, I propose an individualized pedagogy, an aid to the preparation of exam competitions or questions. My goal is to advance my students without overburdening them. I give assignments after each lesson and each student according to their level and I provide useful and effective preparation and revision ideas.
This Mathematics course aims to:
Answer the student's questions (lesson points, exercises and corrections given in class).
Assess the student's comprehension (direct questions, direct application exercises, quizzes, validation of the student's cards).
Verification of the work required of the student:
- Identification of any misunderstandings
- Detailed explanation of issues not included
- Correction of possible errors
- Tips resolution method and tips.
For all the exercises / annals done together, the pupil is strongly encouraged to do everything alone and to write them on their own. The student is also asked to note any difficulties encountered as well as the time spent on each exercise. This approach aims to:
- Validation of assimilation
- The assessment of the writing
- Evaluation of effectiveness (speed, absence of errors, etc.).
Read more
Show more
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verified badge
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This course is for anyone who wants to:

✅ Learn Python from the beginning
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📚 On the program:

Variables

Loops

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Data structures

Practical projects for implementation

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Clear explanations to understand the programming logic

Targeted exercises adapted to your level

Concrete projects to create your own applications

🎯 My goal:

Helping you understand the logic behind the code

Progress at your own pace

Create your own projects in Python and gain independence
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The goal is to understand, practice and gain autonomy through clear explanations and concrete examples.
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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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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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