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Since September 2020
Instructor since September 2020
Translated by GoogleSee original
IOS Application Development Course (Swift Storyboard)
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From 20 € /h
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Do you want to get started in mobile programming? Do you want to create an application for iPhone or iPad? You are in the right place ! In this introductory course, I invite you to create your very first iPhone application in a few hours and from scratch. Yes, it is possible and we will prove it together!
Course objectives:
-Discover the Xcode software
-Take control of the simulator
-Compose an interface with the storyboard
-Discover the basics of the Swift language
-Connect the code and the interface
-Be autonomous thanks to the documentation
-View and resolve errors
Extra information
MacBook or VMWare Workstation on Windows.
Location
location type icon
Online from Tunisia
About Me
Computer engineer with significant experience in computer development. I offer help in the preparation of questions or exams or in learning new technologies.
My goal is to make the student progress without overloading him by presenting him the best methodologies of IT developments.
Tell me what interests you:
- Computer programming course
- Website development,
- Development of mobile applications
- Database management
- Surfing the Internet
- IT development
Education
Computer engineer with significant experience in computer development with a master's degree from ESPRIT.
Private higher school of engineering and technology.
Experience / Qualifications
Intern at ETECTURE Gmbh - 2019
Intern at VERMEG - 2019
Intern at Bonsch Co - 2020/2021
Creation of my own solutions (Lost & Found App and Website)
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Duration
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
To create websites or build the site of your dreams, you need to know how to code in a programming language.
Websites rely on data and logic to do their magic, and that requires a programmer - you - to tell the computer what to do and how.
But how do you write websites, anyway? Where to start?

This course is designed to teach you the basics of HTML5 and CSS and to give you lots of practice along the way! We will be interested in:

* use HTML code;
* structure a web page in HTML;
* format a web page in CSS;
* organize the elements of a web page using CSS;
* modify the layout of an HTML page with CSS;
* integrate formulas into a web page
Read more
This course has been designed to teach you the basics of the Java programming language and allow you to put them into practice through small exercises! We'll start with the basics of programming, before moving on to object-oriented programming. In the last part, you will discover some principles that will allow you to go further.

By the end of this course, you will be able to:

-Manage the variables of a program in Java;
-Use the principles of object-oriented programming in Java;
-Use advanced principles in Java.
Read more
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While adults are still debating whether kids should use AI, they are already using it.
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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

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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

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

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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.

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Most AI courses for kids teach "here's the tool, use it." I teach how to think about AI.
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And, more importantly: that they can control how they use it.

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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.

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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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verified badge
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1- Demystify the modern syntax (ES6+) often used in React.
2- Increase efficiency by using the most powerful syntactic shortcuts.
3- Secure your code to avoid frequent bugs related to missing data.
4- Mastering asynchronicity to manage data calls (API).

📖 Detailed program content

The course is divided into 13 key concepts, illustrated by comparative examples (classic syntax vs. modern syntax) and concrete use cases in React:

1- Ease of writing: Use of Template Literals (`backticks`) for dynamic character strings and Shorthand property names to simplify the creation of objects.

2- Logic and Functions: Mastery of Arrow => Functions (arrow functions) and their implicit return, essential for React components and hooks.

Data manipulation:

1- Destructuring (decomposition) to properly extract data from objects and arrays (e.g., Props and States).

2- Rest & Spread Operators (...) to copy arrays or merge objects without modifying the original (concept of immutability).

Code robustness:

1- Managing default parameter values.

2- Advanced security with Optional Chaining (?.) and Nullish Coalescing (??) to prevent application crashes.

3- Functional Programming: Intensive use of array methods (.map(), .filter(), .reduce(), .find()) to transform data into user interfaces.

4- Architecture and Asynchronism: Code organization via modules (Import/Export) and API request management with Promises and Async/Await.

🛠️ Teaching method: "Learning by doing"

This course is not just about theory. It includes:

The "Interstellar Dashboard" Exercise: A 15-minute thematic case study where students manipulate data from space missions. This allows them to immediately apply destructuring, filtering, and asynchronicity to a real-world project.

The Interactive Quiz: A series of 10 questions designed to validate understanding of each concept before moving on. Each question presents real-world scenarios that developers will encounter in React.

🚀 Learner's result

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What I cover:

Python for beginners and intermediate learners
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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)
Good-fit Instructor Guarantee
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