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Since October 2024
Instructor since October 2024
Competitive Coding, Coding Competetions & Problem-Solving Tutoring
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From 21 € /h
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Ace Competitive Programming: I specialize in helping students master the art of competitive coding. Whether you're preparing for coding competitions like Codeforces, LeetCode, Hackerrank, or university-level contests, I guide you through the problem-solving techniques and strategies used by top coders.

Structured Problem Solving: My tutoring sessions focus on breaking down complex problems into manageable steps. You’ll learn how to approach coding challenges with a clear and logical mindset, enabling you to solve problems efficiently under time constraints.

Algorithm Optimization: Understand and implement key algorithms such as greedy algorithms, dynamic programming, backtracking, and divide-and-conquer. We’ll focus on optimizing your solutions for speed and efficiency, ensuring your code runs within time limits during competitions.

Data Structure Expertise: Gain a deep understanding of how to effectively use arrays, linked lists, stacks, queues, trees, graphs, and other advanced data structures to solve challenging problems. You’ll learn when and how to choose the right data structure to improve performance.

Hands-On Practice: Every session includes real-time problem-solving, where we’ll tackle actual coding problems from popular competitive platforms. I provide immediate feedback on your code and guide you through debugging and refining your solutions.

Improve Speed & Accuracy: Competitive coding is all about solving problems quickly and accurately. I help students develop strategies to think on their feet and avoid common coding pitfalls, while also sharpening their typing and coding speed.

Prepare for Coding Interviews: In addition to competitions, the skills you’ll learn are invaluable for technical interviews at top companies. I provide specific coaching for coding interview prep, ensuring you’re ready to tackle both standard and advanced problems with confidence.

Stay Motivated & Confident: Competitive coding can be tough, but I keep students motivated with challenging yet achievable goals. You’ll develop resilience and confidence in your coding skills, ready to take on any challenge.
Extra information
Must have experience in at least one programming language
Location
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Online from United Kingdom
About Me
Engaging & Passionate Tutor: I believe that learning should be both exciting and meaningful. I thrive on the energy of students who are eager to understand complex concepts, and I am committed to making every lesson engaging and productive.

Personalized Approach: Every student is different, and I adapt my teaching style to meet individual needs. Whether you're a quick learner or need a bit more time, I tailor my lessons to ensure each student fully grasps the material at their own pace.

Hands-On Learning: I focus on practical examples and interactive problem-solving, especially in subjects like Computer Science and Physics. My students don't just memorize formulas—they understand how to apply them in real-world scenarios.

Encouraging & Supportive: My teaching philosophy is centered around encouragement and building confidence. I create a safe space where students feel comfortable asking questions and exploring new ideas without fear of making mistakes.

Structured & Organized: I provide clear lesson plans, assign homework to reinforce key concepts, and regularly assess progress to ensure continuous improvement. Parents and students can expect transparency and structured guidance throughout the tutoring process.

Experienced Tutor: With over two years of experience in Computer Science, Mathematics, and Physics, I have worked with students across a variety of learning levels. My expertise spans programming, algorithms, mathematical reasoning, and physical sciences.

Committed to Long-Term Success: I’m not just focused on short-term exam results—my goal is to foster a deep understanding of the subject matter that will benefit students in their future studies and careers. I equip students with the tools and mindset needed to approach new challenges with confidence.
Education
Student at Manchester Metropolitan University, Completed A-Levels with a B in Mathematics, Completed IGCSE with 6 B's in every subject except Computer Science and Islamiat.
Experience / Qualifications
Software Engineer, Freelance Programmer, Computer Science Expert, Crypto Mining Rigs Expert, Python specialist, C++ specialist, Java specialist, Tutoring Expert, Students Favorite, Teaching Expert, Programming Expert, Competitive Coder
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
30 minutes
45 minutes
60 minutes
90 minutes
120 minutes
The class is taught in
English
Urdu
Hindi
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
Master the Fundamentals: My tutoring sessions are designed to give students a solid foundation in Object-Oriented Programming (OOP) and Data Structures and Algorithms (DSA). Whether you're a beginner or need to strengthen your skills, we will cover the essential concepts that are critical for coding and software development.

Language Flexibility: I offer tutoring in Java, C, C++, and Python, providing flexibility for students to learn in the language they are most comfortable with or the one required for their academic needs. We’ll compare the nuances of these languages to help you develop a deeper understanding of programming concepts.

Comprehensive OOP Concepts: Learn the core pillars of OOP—encapsulation, inheritance, polymorphism, and abstraction—and how to implement them in real-world coding projects. My lessons focus on making these abstract concepts easy to grasp through hands-on practice and examples.

Data Structures & Algorithm Mastery: From arrays, linked lists, and stacks, to more complex structures like trees, graphs, and hash tables, I guide students through the implementation and usage of these critical data structures. We’ll also explore fundamental algorithms like sorting, searching, recursion, and dynamic programming, ensuring you can tackle coding problems efficiently.

Problem-Solving Skills: I focus on building strong problem-solving skills and critical thinking. We’ll work on a variety of programming challenges, competitive coding exercises, and real-world applications that sharpen your logical reasoning and coding proficiency.

Interactive & Engaging Learning: Every session is interactive, with plenty of coding exercises, real-time debugging, and immediate feedback. You’ll walk away with a thorough understanding of how to apply programming concepts in a practical way.

Exam Preparation & Beyond: Whether you're preparing for school exams, coding interviews, or personal projects, my tutoring is geared toward equipping you with the skills and knowledge you need to succeed. I also provide practice problems and quizzes to ensure continuous learning and improvement.
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GCSE Computer Science, Mathematics, and Physics Tutoring

I specialize in tutoring Computer Science, Mathematics, and Physics for GCSE students. I aim to provide clear, engaging lessons that help students master key concepts while building confidence in their problem-solving abilities.

In Computer Science, students will learn programming fundamentals, algorithms, loops, logic gates, conditions, computational thinking, and exam techniques to excel in theory and practical assessments. Whether you're new to coding or refining your skills, I guide students through real-world applications of computing, using languages like Python to bring lessons to life.

In Mathematics, I focus on helping students understand the core principles of algebra, geometry, and statistics. Lessons are designed to reinforce schoolwork, address challenging topics, and prepare students for GCSE exams with practice problems, test-taking strategies, and personalized feedback.

For Physics, I make complex concepts like mechanics, electromagnetism, and energy systems accessible and interesting. Students will engage in hands-on activities and thought experiments that deepen their understanding of the physical world, preparing them for exams and future STEM studies.

I assign targeted homework after each session and provide periodic progress reports to ensure students stay on track and continuously improve. My approach is to challenge students without overwhelming them, fostering a positive learning environment where they can excel academically.
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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 cohort is designed for young people who want to learn in an affordable, flexible, and enjoyable way without having to dedicate a huge amount of time each week or even just extra support.

This beginner-friendly course introduces students to the world of computers and computer science through simple explanation.

Students will learn how computers work, including hardware, software, memory, storage, data, and how a computer processes information. They will then explore how applications are used to create and organize information, with practical experience using tools such as Microsoft Word, PowerPoint, and Excel.

As the course progresses, students will be introduced to important computer science concepts including binary numbers, algorithms, programming, databases, networks, the Internet, and cybersecurity.

By the end of the course, students will have a good foundation in computer science and improved digital skills.
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Additional materials + practice qs | Lesson plans + regular feedback | Recording + session notes available

A common theme running through our lessons will be to simplify - even topics like electromagnetic induction can be reduced to small bits :)

Together, we'll:
1. Find learning gaps
2. Break concepts into small bits
3. Apply to real world and exam questions
4. Work on exam technique - like the difference between "explain" and "describe" questions
5. Build mark-scheme friendly language

“It's not that I'm so smart, it's just that I stay with problems longer”
- Albert Einstein, while studying superposition of waves, I’m sure :)
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I travel without problem throughout the region of Brussels and its surroundings, for lessons of at least 2 hours. For France, courses are only given remotely.

Here are some key words that will be covered in my classes:
Scenario analysis, Year, Rounding, Today, Bdnb, Bdnbval, Bdsum, Search, Column, Copy/paste in values, Copy/paste with transposition, Consolidation, Date, Datedif, Determat, Dollar, Right, Righterg, Equiv, Esterror, Estna, Frequency, Filter (simple and advanced), Format of cells, Left, Large.Value, Printing of documents, Index, Indirect, Inversemat, Day, Weekday, Line, Matrix, Max, Maxa, Max.Si, Min , Mina, Mina.If, Formatting of cells and ranges, Month, Average, Average.If, Nb, Nb.If, Nbval, Naming of cells and ranges, No, Small.value, Product, Productmat, Protection of cells, Lookup (Lookup), Lookupv (VLookup), Lookuph (HLookup), If (If), If.Not.Disp, If.Conditions, Iferror, Sum, Sumproduct, Sum.If, Sum.If.Set, Substitute , Pivot tables, Sorting, Cell locking

Do not hesitate to contact me to organize your lessons according to your needs and availability. Together, we will develop your Excel skills in an efficient and personalized way.
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If you’ve ever felt that science and math are difficult, it’s probably because no one showed you how to think like a problem solver.
In my classes, you’ll learn not just formulas or code but how to truly understand concepts, apply them, and build strong logical intuition.

I teach:
• 🔢 Mathematics: From algebra and calculus to applied problem-solving for real-world use.
• 💻 Computer Science: Coding fundamentals (Python, C++), algorithms, and logical thinking for beginners and intermediate learners.
• ⚛️ Physics: Mechanics, thermodynamics, and practical examples that make abstract ideas simple and visual.

As a Software Engineer and Master’s student in Engineering at Nagoya University, I bring both academic knowledge and hands-on experience from real projects. My teaching approach is interactive, visual, and deeply focused on understanding over memorization.

Let’s turn complex problems into clear, step-by-step insights — and make learning something you genuinely enjoy.
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I am a Math and Physics tutor with over 10 years of experience, backed by an MIT background and training from top technical schools. My lessons are unmatched, blending charisma, passion, and innovative teaching methods to create a magical learning experience. I focus on highly personalized, one-on-one sessions, understanding each student’s unique mindset to unlock their full potential.

Teaching is my true passion! I genuinely enjoy sharing my knowledge and skills with those in need. My friendly and supportive attitude allows me to establish a connection with every student right from the first lesson. So, if you're feeling demotivated and down, don't worry! I'm here to help you find the motivation you need for both academic and personal success.

If you have any questions, please feel free to contact me anytime here. I will respond to you as soon as possible!
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Sporting is one of the best ways to forge a steel mind.
This requires intense work, done regularly and assiduously. We will work so that you become the best version of yourself.

I will base my work on the skills of each and its goals so that everyone can feel comfortable in their bodies and in their minds.
The sessions will be adapted to your physical skills so that you can improve over the long term, we will always focus on the quality of work in the face of the amount that is not always synonymous with good work.
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In our imagination, nourished by our education and our conditioning, we all believed one day that once married, having founded our family, we will realize our dream.

Before taking the leap, we all idealize this life, and the stronger our hope, the stronger the awakening to the reality of life would probably be.

Indeed, family life is another experience, a challenge and a process during which we learn a lot about ourselves, about the other and about the relationship.

By choosing the right mediator, family and married life can therefore be a great opportunity to grow (sometimes in pain, in joy).

Among the possible accompaniments, some examples:
- The basics of a solid and fulfilled married or family life
- How to resolve disagreements
- The secrets of a balanced and balancing child education
- Learn to express emotions
- Manage families and in-laws
- How to take your place within the couple
- Define the respective roles of each

Among the techniques used:
- Non-violent communication tools

- The “helping relationship” which aims to support people in crisis or conflict situations and help them resolve dysfunctions (emotional, relational difficulties, repetition of scenarios, lack of self-confidence, etc.) . It also leads to better self-knowledge and guides the person to discover their own solutions.
The practitioner in helping relationship through his attentive and neutral listening, his training, his personality, his presence, his availability will allow the consultant to progress towards a better knowledge of his psychological mechanisms, respecting his blockages, his rhythm and his ability to hear.
The therapist takes into account the subject as a whole: physical, psychological and social.
Allow the consultant to access autonomy so that he can, when he feels ready, walk alone, in all inner serenity.

- Coaching that allows you to act and take responsibility, which is the basis of effective and lasting change. Work on the management of emotions and relationships, confidence and self-esteem, self-affirmation is at the center of individual support, which allows you to live more fulfilled and authentic relationships.

- Love coaching, which is personalized support based on understanding the mechanisms of love and focusing on putting an individual in this disposition of openness and acceptance of the other and their difference (which prepares to receive love) and to transfer keys of understanding to gain emotional maturity.
It allows you to obtain concrete and measurable results in your love life.
Through coaching, the consultant becomes aware of his resources, optimizes his potential and transforms his life.

- Brief marital and family therapy. She is interested in how the problems manifest themselves in the present but the personal history of each is also approached. This insight helps to better understand the events experienced and to make connections between the past and what is at stake in the current difficulties. We introduce changes in behavior to open up perspectives, mainly relational.

- Sophrology. Sophrology is a gentle method that promotes relaxation of the body and mind where you relearn how to listen to your feelings using controlled breathing exercises, muscle relaxation and mental suggestions. It allows you to develop your potential, regain self-confidence, manage your emotions and achieve your goals. It will be, at times, interesting to use this technique to manage to unblock the mind that is blocking and to give the body more amplitude to welcome what presents itself.

➤ THE COACH PSYCHOANALYST PSYCHOPRACTITIONER
Trained at the Grande Ecole post-preparatory classes & Ivy League University in the United States, our psychoanalyst, psycho-practitioner and behaviorist coach has specialized and worked for more than 15 years, in Europe and North America, in the field, in renowned international public and private establishments, regularly speaking in forums and conferences, and also offering personalized INDIVIDUAL support.

➤ PLACE, TIMETABLE, PRICES
✓ Locations: Geneva-Lausanne-Fribourg-Zurich-Neuchâtel-Lugano-Montreux-Basel-Neuchâtel-Bern-Lucerne-Brussels-Luxembourg-Paris-Lyon. But currently, these sessions continue to be offered by videoconference in the current context and in accordance with general demand which is almost unanimous on this subject.

✓ Indeed, apart from the classic advantages of videoconferencing (time saving related to travel & their unforeseen events, eco-responsibility, increased schedule flexibility, etc.), the quality of the session & the interaction remain identical. In addition, the entire exchange, notes and recommendations are immediately transcribed on the dedicated chat.

✓ To support us among ourselves & to be pleasant to you in this sustainable / particular period and in a spirit of solidarity, the fees are temporarily reduced and will not increase after the start of our sessions.

✓ Languages: French/English.

✓ The progress following these private sessions is perceptible from 1 to 2 sessions (*2024 study).

✓ As other people do regularly, you can also please your loved ones by offering gift vouchers available all year round.

CONTACT / PROGRAM
✓ First contact by email then by phone.
✓ A la carte program: evaluated and adapted to each need.

➤ The Apprentus calendar is not refreshed/updated.
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Are you a university student, engineer, or professional who needs to actually use data — not just learn theory about it?
This course is built around real problems and real code. We skip the textbook formulas and go straight to applying statistics and data science the way professionals do: with Python (pandas, NumPy, scikit-learn, matplotlib) and R (RStudio).
What we cover, adapted to your level and goals:
- Descriptive and inferential statistics (the ones that actually matter)
- Data cleaning, exploration, and visualization
- Regression, classification, and intro to machine learning
- Time series and forecasting basics
- R for statistical analysis and academic research

Who this is for:
- University students in statistics, economics, engineering, or biology
- Professionals wanting to move into data analysis or data science
- Researchers who need to process and present data properly

I use Python and R professionally as a working engineer — everything I teach comes from real application, not just academic exercises.
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I offer private lessons in Spanish, we can do it online“Learn Spanish from Zero – Simple, Supportive, and Enjoyable Lessons!” Learn to Communicate with Ease.
I'm ready to adapt our study plan to your objectives and demands!
I provide personalized methods that will take you step by step to meet your goal! I am dynamic, easy-going and full of energy!

Start your first lesson today!
All material will be provided to you by email.
Lessons are well organized
I can suggest a weekly task
I'm a highly qualified teacher who has a master degree in language learning plus that I have been teaching for 13 years.
Learning a new language can open new doors to your career path.
I can lead you to pass DELE.
Language of teaching can be English, French, Arabic and Spanish.
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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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Experienced and patient teacher of logic for computer science.

I have taught logic, formal languages and automata theory to undergraduates for six years. My tutoring is adapted to the student's level and goals. Whether you need to learn logic for your studies, or you would simply like to know more about the subject, I will be more than happy to help you improve your understanding and skills.

Logic
The sciences presuppose a certain standard of rationality. An ability to distinguish between correct reasoning and claims that do not follow from the assumptions. In this class we study the basic principles of logic and apply mathematical techniques to the study thereof.
Topics include:
Propositional and Predicate Logic
Syntax and semantics
Natural deduction
Semantic tableaux
Correctness and soundness
Completeness

Formal languages and automata
A formal language is an abstraction of general characteristics of programming languages. Such a languages consists of a set of symbols together with some rules to determine whether a string made up out of those symbols is a member of the language.

Topics include:
Regular languages, context-free languages
Finite automata, pushdown automata, Turing machines
Regular expressions
Regular grammar, context-sensitive grammar
Pumping lemmas for regular and context-free languages
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I teach Python specifically for finance and data applications - the kind used in economics, business analytics, and quantitative programs. This isn't a general "learn to code" course; it's built around real financial data, benchmarking, and the workflows you'll actually use in coursework or early career work.

Topics include:
Python fundamentals through a finance lens (data structures, functions, control flow).
Working with financial data and datasets.
Performance benchmarking and writing efficient code.
Applying concepts from Hilpisch's Python for Finance.
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Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
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Session 1: Revolutionizing your Scientific Writing with LaTeX & AI
Duration: 2 Hours | Level: Beginner | Tools: Overleaf + AI**

First Hour: Foundations and Cloud Environment (60 min)

1. Introduction to LaTeX Philosophy (15 min)

- The "WYSIWYM" concept:** Explain the difference between Word (*What You See Is What You Get*) and LaTeX (*What You See Is What You Mean*). Why content takes precedence over form.
- Key advantages:** Unrivaled typographic quality, automatic reference management, stability on long documents (theses), and free of charge.
- The structure of a file:** Distinction between the **preamble** (the brain: settings and packages) and the **body of the document** (the heart: text).

2. Immersion in Overleaf (25 min)

- Configuration:** Creation of an account and first project "Blank Project".
- Exploring the interface:** The file panel (left), the code editor (middle) and the PDF preview (right).
- Real-time collaboration:** How to share a project and leave comments (like on Google Docs).
- History and versions:** How to revert to a previous version in case of a compilation error.

3. Practical Workshop: My First Document (20 min)

* Writing basic commands: `\documentclass`, `\usepackage[french]{babel}`, `\title`, `\author`.
* Compilation of the document and observation of the result.
* Structuring: Use of `\section` and `\subsection`.

Second Hour: Mathematics and the Magic of AI (60 min)

4. The Power of Mathematics (20 min)

- Mathematical modes:** Difference between the text (`$...$`) and the centered block (`\[...\]`).
- Essential syntax:** Fractions `\frac{}{}`, exponents `^`, indices `_`, and roots `\sqrt{}`.
- Introduction to AMS packages: Why amsmath and amssymb are essential for professional rendering.

5. From hand to screen: AI at the service of LaTeX (30 min)

- Presentation of OCR tools:** Use of **Mathpix Snip** (the leader) or models like Gemini/ChatGPT to transform a photo into code.
- Concrete demonstration:
1. Take a picture of a complex handwritten formula (e.g., an integral with matrices).
2. Use AI to generate the corresponding LaTeX code.
3. Correction and insertion: Learn to check the AI-generated code before copying and pasting it into Overleaf.

6. Conclusion and Q&A (10 min)

* Summary of achievements.
* Resources for further exploration
* Definition of the exercise for the next session.
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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 cohort is designed for young people who want to learn in an affordable, flexible, and enjoyable way without having to dedicate a huge amount of time each week or even just extra support.

This beginner-friendly course introduces students to the world of computers and computer science through simple explanation.

Students will learn how computers work, including hardware, software, memory, storage, data, and how a computer processes information. They will then explore how applications are used to create and organize information, with practical experience using tools such as Microsoft Word, PowerPoint, and Excel.

As the course progresses, students will be introduced to important computer science concepts including binary numbers, algorithms, programming, databases, networks, the Internet, and cybersecurity.

By the end of the course, students will have a good foundation in computer science and improved digital skills.
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Additional materials + practice qs | Lesson plans + regular feedback | Recording + session notes available

A common theme running through our lessons will be to simplify - even topics like electromagnetic induction can be reduced to small bits :)

Together, we'll:
1. Find learning gaps
2. Break concepts into small bits
3. Apply to real world and exam questions
4. Work on exam technique - like the difference between "explain" and "describe" questions
5. Build mark-scheme friendly language

“It's not that I'm so smart, it's just that I stay with problems longer”
- Albert Einstein, while studying superposition of waves, I’m sure :)
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