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Since November 2022
Instructor since November 2022
Introduction to Python for Quantitative finance and investing
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From 78 C$ /h
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Python is one of the most powerful programming/scripting tools of our times. However, learning from textbooks or online can become time consuming and inefficient, since we easily lose focus. In this class, we learn by doing. As you learn new techniques and concepts, you will immediately use them to build tools that will help you become a data-driven and informed investor or trader. At the end of the class, you are left with a basic understanding of what python can do, but also with a template code that you can later use to build you own strategies on top.

The class in a nutshell:

(1) How to extract market data with python, such as: OHLC quotes, volume, dividend history, financial history etc.
(2) How to store and handle data efficiently.
(3) How to build and group your own portfolio based on any custom metric (e.g. P/E ratio, Debt/Equity ratio, dividend etc)
(4) How to build future paths of a stock in our portfolio, via Monte Carlo simulations, based on your own beliefs (e.g. optimistic/pessimistic).
Extra information
A laptop with a connection to the internet is required. The rest can be worked out.
Location
location type icon
Online from Netherlands
About Me
I come from Greece, but I moved in the Netherlands more than five years ago where I studied and obtained my PhD in High Energy Astrophysics. Since then, I have been working as a quantitative analyst for a big bank in the Netherlands. I have ample experience in building algorithms and quantitative tools for trading and investing using python. My main focus is derivative (option) trading and valuation, but that is a long discussion.The environment in my classes is very friendly and not formal at all, and I always welcome a nice conversation about the financial markets, philosophy or life in general.
Education
BSc, Physics ( Aristotle University of Thessaloniki, Greece)
MSc, Computational Physics ( Aristotle University of Thessaloniki, Greece)
PhD, Computational Astrophysics (University of Groningen & University of Southampton)
Experience / Qualifications
Blockchain application developer ( IT ), Groningen, the Netherlands
Quantitative analyst ( IT, Banking) , Utrecht, the Netherlands
Age
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Duration
60 minutes
The class is taught in
English
Greek
Availability of a typical week
(GMT -05:00)
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
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Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

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► EXPERT BUSINESS, ACCOUNTING, FINANCE & ECONOMICS TUTOR FROM SWITZERLAND
► IB, A-LEVEL, IGCSE & INTERNATIONAL SCHOOL SUPPORT

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► Clear digital notes
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► Flexible lesson structure
► Focused one-to-one support from Switzerland

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► MY GOAL

My goal is not only to improve grades, but to help students become confident, independent and analytical thinkers. With the right guidance, Business, Accounting, Finance and Economics become logical, practical and much easier to master.

► SUBJECTS: Business Studies, Accounting, Finance, Economics, Business Administration, Management
► LEVELS: IGCSE, A-Level, IB, International School, Foundation Courses, University Modules
► FORMAT: Online tutoring from Switzerland
► FOCUS: Deep understanding, exam confidence, analytical thinking and long-term academic success.
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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
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• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
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• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
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• 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
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12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
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-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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Learn Python with a structured, hands-on approach! Whether you are a absolute beginner looking to start coding, a student needing help with coursework, or a professional aiming to automate tasks, these lessons are tailored for you.What we will cover:
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Attualmente sto frequentando un Master in Finance e ho una solida preparazione accademica in matematica, economia, statistica e finanza.
Mi piace condividere le mie conoscenze e aiutare gli studenti a comprendere anche gli argomenti più complessi in modo chiaro e intuitivo. Il mio approccio è paziente, strutturato e personalizzato in base alle esigenze e al ritmo di apprendimento di ciascuno studente.
Credo che un buon tutor non debba limitarsi a spiegare i concetti o a fornire le risposte corrette, ma debba soprattutto aiutare gli studenti a sviluppare un metodo di studio efficace, acquisire sicurezza e diventare autonomi nella risoluzione dei problemi.
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As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

Beyond these general principles, there is no magic rule or method. Some approaches work with some students but not with others. Adaptation to individual needs is therefore the main objective of private lessons. So I will do my best to find what motivates and helps my student.
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Learn to code with method and logic
Whether it's to succeed in the NSI (Digital Sciences and Technology) specialization in high school, design personal projects, or prepare for higher scientific studies, mastering code relies on solid algorithmic thinking. I help students understand the structure of programming languages and the logic of data.

Subject areas and languages taught:

Algorithms & Logic: Designing data structures and solving problems.

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Data Management: Analysis and SQL queries / databases.

Basic Web Development: HTML & CSS for creating structured pages.

The goal is to take the student from simply writing code to true autonomy in development.
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Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance.
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What we can cover depending on your goals:

Python fundamentals: variables, loops, functions, data structures
- Object-oriented programming (OOP)
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I'm not a student teaching on the side — I'm a professional engineer who uses Python daily for data analysis, modeling, and automation. I know exactly which concepts matter in the real world and which ones you can skip for now.
Sessions are 100% personalized: I adapt the pace, the examples, and the exercises to your background and your goal — whether that's passing your university exam, building a project, or landing a job.
verified badge
The teaching methodology will be based on your course content and your objectives. My aim is primarily to help you with the concepts and application of that knowledge when submitting assignments and answering questions. I am passionate about teaching. I love sharing my knowledge and experience with my students. I am a life long learner and deeply passionate about books and learning new things. I teach students at High school, Undergraduate and Graduate levels.
verified badge
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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Do you want to learn programming but don't know where to start? Are you a beginner looking for clear explanations, or do you want to improve your coding skills with personalized support?

This private tutoring program is designed to help you master Python, one of the most versatile and easy-to-learn programming languages. Through a step-by-step approach, interactive exercises, and hands-on projects, you'll gain the confidence and skills to write code effectively and solve real-world problems.

💡 Why Choose this Program?
With these personalized courses, you will:
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🚀 Learn at your own pace – Whether you’re a complete beginner or want to refine your skills, lessons adapt to your level.
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🐍 Develop problem-solving skills – Learn how to break down complex tasks and write efficient code.
🎓 Prepare for exams, jobs or personal projects – Whether you are a student, professional or enthusiast, Python is an essential skill.
💻 Get live support – Get real-time feedback, coding tips, and answers to your questions.
This interactive, hands-on learning experience ensures you understand Python while having fun coding!

📚 What Will You Learn?
This program covers everything you need to become comfortable with programming in Python:

✅ Python Basics (Great for Beginners)
Introduction to Python and installation of the development environment 🖥️
Variables, data types and user input 🔢
Operators and expressions 🧮
Conditional structures (if-else) and loops (for, while) 🔄
Writing and calling functions 🏗️
🚀 Intermediate Concepts (To Go Further)
Lists, Tuples, Dictionaries – Understanding Data Structures 📊
File Management – Reading and Writing Files 📄
Exception Handling – Making Your Code More Robust ⚠️
Introduction to modules and libraries 🏛️
🎯 Advanced Concepts (For the More Ambitious)
Object-Oriented Programming (OOP) – Classes, Objects, Inheritance 🏗️
Recursion and algorithm design 🧠
API and Web – Connecting Python to the web 🌐
Introduction to Databases – Storing and Retrieving Data 🗄️
🎨 Practical Projects & Applications
Creating simple games 🎮
Automation of repetitive tasks 🔄
Data analysis with pandas 📊
Web scraping and working with APIs 🌍
Introduction to Artificial Intelligence and Machine Learning 🤖 (optional for advanced learners)
🎯 An Interactive and Fun Learning Experience
Live Online Classes – Learn from home with interactive sessions via screen sharing.
Tailor-made lessons – Content adapts to your level for an effective and personalized learning journey.
Practical exercises and projects – Less theory, more practice! Learn with real-world examples.
Caring and motivating environment – No pressure, no judgment: progress at your own pace.
Practical application – Each concept learned is applied immediately through exercises and mini-projects.
🔔 For Who?
This program is ideal for:
✅ Complete beginners – If you’ve never written a line of code, no worries! The lessons start from scratch.
✅ Students – To prepare for programming courses, computer science exams or competitions.
✅ Self-taught – If you want to add Python to your skillset, this course offers you structured support.
✅ Professionals and people in retraining – Python is a sought-after skill in data science, automation and web development.
✅ Tech Enthusiasts – Curious to learn code? This course makes learning Python both fun and useful.

🚀 Ready to Embark on Your Python Adventure?
Join the "🐍 Personalized Private Lessons – 💻 Learn to Code and Program in Python!"
Gain the skills and confidence to write clean, efficient, and powerful code.

Sign up today and take your first steps into the exciting world of Python programming! 🔥
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This course is designed for anyone interested in learning data science using Python. It provides a hands-on introduction to fundamental data analysis tools such as NumPy, pandas, matplotlib, and seaborn. You'll learn how to manipulate datasets, create visualizations, and lay the foundations for statistical analysis and machine learning.

The course combines theory and practical exercises for effective, practical progress. No prior programming experience is necessary: we'll start with the basics to build solid, usable skills quickly.
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► EXPERT BUSINESS, ACCOUNTING, FINANCE & ECONOMICS TUTOR FROM SWITZERLAND
► IB, A-LEVEL, IGCSE & INTERNATIONAL SCHOOL SUPPORT

I completed my Master’s degree in Business Information Systems at a Swiss University of Applied Sciences, where my academic background strongly combined business, finance, economics, mathematics, analytical thinking and problem-solving. This technical and business-oriented foundation shaped the way I teach today: clearly, logically and with a strong focus on real understanding.

For many years, I have successfully supported students in Business Studies, Accounting, Finance and Economics. As a tutor from Switzerland, I bring a structured, patient and highly individual teaching style to students who want to improve their grades, prepare for important exams and build real confidence in business-related subjects.

My lessons are designed for students who want more than just memorizing definitions and formulas. I focus on understanding business concepts, applying financial logic, interpreting economic relationships and solving accounting problems step by step.

► EXAM PREPARATION & ACADEMIC SUPPORT

► IGCSE BUSINESS, ACCOUNTING & ECONOMICS
I help students build a strong foundation in business concepts, financial calculations and economic thinking. My lessons include clear theory explanations, topic-by-topic revision and exam-style practice to prepare students confidently for their assessments.

► A-LEVEL BUSINESS, ACCOUNTING, ECONOMICS & FINANCE
A-Level subjects require strong analytical thinking, clear written arguments and confident application of theory to real business situations. I support students with structured revision, essay technique, calculation practice, case-study analysis and preparation for demanding exams.

► INTERNATIONAL BACCALAUREATE — IB BUSINESS MANAGEMENT & ECONOMICS
I provide structured support for IB students, focusing on deep understanding, strong terminology, analytical writing and exam technique. Students learn how to explain concepts clearly, analyse case studies, evaluate decisions and structure high-quality answers.

► INTERNATIONAL SCHOOL PROGRAMMES
I support students following international, bilingual and private school curricula in Business Studies, Economics, Accounting, Finance, Commerce and Management. Since every school can have different expectations, I adapt my teaching to the student’s syllabus, textbook, teacher requirements and exam format.

► UNIVERSITY & FOUNDATION COURSES
For older students, I offer support in Accounting, Corporate Finance, Economics, Business Administration, Financial Management, Statistics and quantitative business modules. I help students understand difficult theory, solve assignments, prepare for exams and develop strong analytical skills.

► HOW I TEACH

► I FOCUS ON REAL UNDERSTANDING, NOT MEMORISATION.
I explain business, accounting and economic concepts step by step so students understand the logic behind the theory and can apply it confidently in exams.

► I CONNECT THEORY WITH REAL BUSINESS EXAMPLES.
Business and economics become easier when students can see how concepts work in real life. I use practical examples to make topics clear, relevant and memorable.

► I SIMPLIFY COMPLEX CALCULATIONS.
In Accounting and Finance, I break calculations into clear steps so students understand what is given, which formula is needed and how to reach the final answer correctly.

► I TRAIN EXAM TECHNIQUE CAREFULLY.
Students learn how to structure answers, use key terminology, analyse case studies, justify decisions and manage time under exam conditions.

► I ADAPT EVERY LESSON TO THE STUDENT.
Every student learns differently, so I adjust my explanations, pace, examples and practice questions to match the student’s exact level and goals.

► YEARS OF EXPERIENCE WITH IB, A-LEVEL, IGCSE & INTERNATIONAL SCHOOL STUDENTS

Over the years, I have successfully supported students from prestigious schools and demanding academic programmes, helping them strengthen their understanding, master exam techniques and achieve excellent progress in Business Studies, Accounting, Finance and Economics.

► ONLINE LESSONS

► Interactive whiteboard
► Clear digital notes
► Personalised exercises
► Exam-style practice
► Flexible lesson structure
► Focused one-to-one support from Switzerland

Online lessons are structured, personal and highly effective. Students can ask questions directly, follow every step in real time and review the materials after the lesson.

► MY GOAL

My goal is not only to improve grades, but to help students become confident, independent and analytical thinkers. With the right guidance, Business, Accounting, Finance and Economics become logical, practical and much easier to master.

► SUBJECTS: Business Studies, Accounting, Finance, Economics, Business Administration, Management
► LEVELS: IGCSE, A-Level, IB, International School, Foundation Courses, University Modules
► FORMAT: Online tutoring from Switzerland
► FOCUS: Deep understanding, exam confidence, analytical thinking and long-term academic success.
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This Forex Trading course aims to provide an in-depth understanding of the foreign exchange market and to develop a rigorous analytical methodology based on technical and fundamental principles.

The program covers:

• Introduction to the Forex market (structure, operation, participants)
• Reading and interpreting graphs (market structure, trends, accumulation/distribution phases)
• Advanced technical analysis (support and resistance levels, chart patterns, breakouts, retracements, technical indicators)
• Fundamental analysis (macroeconomic data, monetary policies, impact of central banks)
• Professional Risk Management and Money Management
• Development of a structured trading plan
• Practical case studies in real markets

Training aims :

✔ Master the basics and mechanisms of the Forex market
✔ Develop an independent technical and fundamental analysis capacity
✔ Apply disciplined and optimized capital management
✔ Build a consistent and measurable trading strategy

The training is aimed at beginners wishing to acquire a solid foundation as well as intermediate traders wishing to structure their approach professionally.
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I teach machine learning, AI and Python online to university students, postgraduates, career changers and serious beginners across the UK and Europe. Lessons are in English.

I hold an MSc in Electronics and Electrical Engineering with Distinction and I teach as a Visiting Lecturer on a Master's level module covering data analytics, machine learning and generative AI at a UK university. I also have two accepted international conference papers on deep learning for image classification. I set and mark postgraduate assignments myself, so I know where marks are won and lost on this kind of work.

Who this is for

Undergraduates and postgraduates on AI, ML, data science or computer science modules at any European university. Final year, Master's and thesis students working on a machine learning project. IB and A Level students moving into computing or engineering. Professionals retraining for data roles. Complete beginners who want to learn Python properly rather than copying it from videos.

I work with students on UK, IB and continental European programmes. I have tutored engineering students in Germany and international school students across several countries, so an unfamiliar syllabus or a module taught in a different structure is not a problem. Send me the material and I will work from it.

What we cover

Python for data science with NumPy, Pandas, Matplotlib and scikit-learn. Deep learning using TensorFlow and Keras. Core theory including regression, classification, clustering, decision trees, random forests, neural networks and CNNs, together with the linear algebra, calculus and statistics underneath them. Computer vision and image classification, which is my published research area. Model evaluation, overfitting and hyperparameter tuning. Writing machine learning work up to academic standard, covering methodology, results and critical evaluation.

How lessons work

Send me your module handbook, assignment brief, thesis spec or the code that will not run, and I plan the session around it before we meet. Nothing generic.

In the lesson I explain the concept with a worked example, then you take the keyboard while I watch and correct, because you learn far more doing it than watching me do it. You finish with annotated notes and a clear next step, and you can message me between sessions with questions.

Practical details

Online over Google Meet or Zoom with screen sharing and a shared whiteboard. Sessions run 60 or 90 minutes. I am based in the UK and teach across GMT and Central European time, with evening and weekend slots that suit students anywhere in Europe.

Tell me your course, your deadline and exactly where you are stuck, and I will come back with a plan for the first 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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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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Learn Python with a structured, hands-on approach! Whether you are a absolute beginner looking to start coding, a student needing help with coursework, or a professional aiming to automate tasks, these lessons are tailored for you.What we will cover:
Core Fundamentals: Variables, loops, functions, and data structures.Object-Oriented Programming: Building reusable and clean code.Real-World Projects: Creating scripts, data analysis, or web scraping based on your goals.

Problem Solving: Learning how to debug and think like a programmer. Lessons are highly interactive. We will write code together from day one, and you will receive practical exercises after every session to build your confidence.
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Attualmente sto frequentando un Master in Finance e ho una solida preparazione accademica in matematica, economia, statistica e finanza.
Mi piace condividere le mie conoscenze e aiutare gli studenti a comprendere anche gli argomenti più complessi in modo chiaro e intuitivo. Il mio approccio è paziente, strutturato e personalizzato in base alle esigenze e al ritmo di apprendimento di ciascuno studente.
Credo che un buon tutor non debba limitarsi a spiegare i concetti o a fornire le risposte corrette, ma debba soprattutto aiutare gli studenti a sviluppare un metodo di studio efficace, acquisire sicurezza e diventare autonomi nella risoluzione dei problemi.
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