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Private lesson learning the python language.

From 31 C$ /h

The python programming language is used in a lot of applications, web, servers, video games, data analysis...
Whether it is to learn a language and get started in a project or for tutoring, I offer you as an independent developer and full stack developer student at Zone01 Rouen, a course to understand all the fundamentals of the Python language and OOP (object-oriented programming)

Location

Online from France

About Me

I am a developer, with a real interest in back-end and software.
I offer my services in software development as well as as a programming tutor/teacher.
I registered on Apprentus so that I could share my knowledge while putting it to good use.

Education

Self-taught - I have been developing this skill, which is programming, since I was very young, before the age of 10.
-Lycée les Fontenelles in Louviers (Scientific and digital curriculum)
- Zone01 - FullStack Developer (Apprenticeship)

Experience / Qualifications

I have already given several private lessons but I want more than anything to improve myself in order to produce the best possible content for my lessons, for that I will be very grateful if you give me your opinions.

Besides my former and current students, I am currently a full-stac developer.

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

60 minutes

The class is taught in

French

English

Skills

Python

Reviews

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Video game creation courses, beginner and intermediate support.

39 C$ /h

As a programmer and having already developed several games, notably for GameJams (video game creation competitions), and still currently working on a video game.

I offer support in the creation of your projects.
Whether it be:
- Introduction to video game creation
- Improvement
- Support for a particular game

To help with this process, access to a learning platform (De ma facture) is included, where you will find a written and summarized version of our courses and interviews as well as additional exercises to continue working between our courses.

Feel free to ask questions or request more details about the course offered, I will be happy to answer them!

Matías

4.88

(85)

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.

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

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.

Amin

4.88

(85)

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.

Salah

4.88

(85)

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.

Amine

4.88

(85)

Discover programming lessons suitable for children! With a fun and educational approach, my lessons allow young minds to dive into the fascinating world of programming. Provide your children with an enriching learning opportunity in a fun and stimulating environment.

Adam

4.88

(85)

🐍 Python Course – Learn to code and create your projects!

This course is for anyone who wants to:

✅ Learn Python from the beginning
✅ Strengthen their programming skills

📚 On the program:

Variables

Loops

Functions

Data structures

Practical projects for implementation

💡 How does the course work?

Clear explanations to understand the programming logic

Targeted exercises adapted to your level

Concrete projects to create your own applications

🎯 My goal:

Helping you understand the logic behind the code

Progress at your own pace

Create your own projects in Python and gain independence

Ali

4.88

(85)

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.

Ammar

4.88

(85)

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.

Baia

4.88

(85)

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

Pallavi

4.88

(85)

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.

Cedric

4.88

(85)

Do you want to learn Python, discover programming, or strengthen your basic skills?
As a computer science teacher with a Master's degree in Computer Science, specializing in Data Science, I offer courses tailored to your level and objectives.
We can work specifically on the basics of Python, variables and data types, conditions, loops, functions, data structures, files, as well as problem-solving and first projects in Python.
My approach is primarily practical: I explain concepts gradually, with simple examples, and then we apply them through exercises. The content and pace are tailored to each learner's difficulties and objectives.
The courses are aimed at beginners, students or adults wishing to learn Python or consolidate their programming knowledge.

Moetez

4.88

(85)

You know how to write Python scripts, but your code quickly becomes difficult to read and maintain? Object-oriented programming is the most common way to organize a program, and it's essential in business, engineering school, and technical interviews. I'm a software engineer and I use Python in production (FastAPI, data, optimization). I'll teach you OOP in a practical way: using classes when they simplify the code, and knowing when to do without them when they complicate it.

Program (adapted to your level):

Clean code: why programming paradigms exist and how they make code maintainable
Classes and objects: attributes, methods, the `__init__` constructor, `self`. Differences between OOP and the functional approach
Encapsulation: protecting an object's data, private attributes, properties (@property)
Abstraction: hiding complexity and exposing a simple interface. Abstract classes (abc)
Inheritance: reusing and specializing class behavior, super(), when to avoid it
Polymorphism: the same call, different behaviors. Special methods (__str__, __eq__...)
Final project: model a real problem from A to Z (game, library management, reservation system...)
with a code organized into classes

For who ?

Students (BTS, BUT, Bachelor's degree, engineering school, preparatory classes) preparing for an exam or project; High school students

Career changers and junior developers who want to take their skills to the next level

Prerequisite: knowledge of the basics of Python (variables, conditions, loops, functions). Otherwise, a refresher course can be started.

Each session alternates between short explanations and practical exercises. I can also help you directly with your practical work and projects.

Feel free to contact me to discuss it!

Said

4.88

(85)

I offer personalized lessons adapted to each student’s level, objectives, and learning pace. Whether you want to strengthen your fundamentals, prepare for an exam, improve your academic performance, or develop practical skills, I provide clear explanations and hands-on exercises.

📐 Mathematics: algebra, functions, equations, calculus, probability, and applied mathematics.

💰 Finance: financial analysis, corporate finance, investments, financial markets, portfolio management, risk management, and quantitative finance.

📊 Statistics: descriptive statistics, probability, hypothesis testing, correlation, regression, data analysis, and interpretation of results.

💻 Microsoft Office: Excel, Word, and PowerPoint — from basic to advanced level, including formulas, data analysis, charts, professional documents, reports, and presentations.

As a Master’s student in Finance at the University of Neuchâtel, with a background in Finance and experience in quantitative analysis and credit-risk modelling, I focus on making complex concepts simple, practical, and easy to understand.

Courses available in French or English.

Mariam

4.88

(85)

This course is designed for students and adolescents who want to build a strong beginner-friendly foundation in Computer Science.
Whether you're completely new to Computer Science or need help with a specific programming subject, the course can be customized to match your needs.
Need help with C++ or programming? You can provide me with your syllabus, course outline, or the topics you're studying, and I'll tailor the lessons around what you need to learn.
Learning with friends? Group lessons are also available, allowing you to learn together while following the same customized course.

Nourhan

4.88

(85)

Welcome! Whether you are a complete beginner or a university student aiming to strengthen your technical foundation, this course is tailored to help you master Python programming, database design, and SQL queries effectively.

In our sessions, we focus on hands-on practical learning rather than passive theory. Here is what we can cover:
• Python Programming fundamentals, data structures, and logic building.
• Relational Databases design, schemas, and ER diagrams.
• Writing clean, optimized SQL queries (SELECT, JOINs, aggregations, subqueries).
• Hands-on practice with real-world datasets and troubleshooting common errors.
• Assistance with university coursework, exam prep, and personal projects.

Lessons are tailored to your pace and goals, ensuring you build confidence in writing code independently. Feel free to reach out with any questions before booking!

Pratiksha

4.88

(85)

Python Level 1Workshop

**Course Description**

Want to learn programming from scratch? This beginner-friendly Python course introduces students to coding through interactive lessons, practical exercises, and fun mini-projects.

Students will learn how to write Python programs, work with different types of data, take user input, make decisions using conditions, and repeat actions using loops. Each concept is explained step by step with real-life examples and hands-on coding activities.

**What You Will Learn**

* **Lesson 1: Your First Python Program** — Learn the `print()` function, display messages, and create simple programs.
* **Lesson 2: Variables and Data Types** — Store and work with text, numbers, and other basic data.
* **Lesson 3: User Input and Calculations** — Build interactive programs that accept user input and perform calculations.
* **Lesson 4: Conditional Statements** — Use `if`, `elif`, and `else` to make programs respond to different situations.
* **Lesson 5: Loops and Mini-Projects** — Use loops to repeat actions and apply your skills in practical coding challenges.

**Hands-On Projects**

Students will apply their learning by building beginner-friendly projects such as a calculator, a number-guessing game, and a Rock-Paper-Scissors game.

**Who Is This Course For?**

* Children and teenagers aged 8–16.
* Complete beginners with no prior programming experience.
* Students who want to develop logical thinking, problem-solving, and computational skills.
* Learners who want to explore programming through practical projects.

**My Teaching Approach**

I explain concepts step by step, use relatable examples, and encourage students to write their own code instead of simply copying solutions. Lessons are adapted to each student's pace, with coding exercises and challenges to strengthen understanding.

**No prior coding experience is required.** Students need a computer and an internet connection to participate.

By the end of Level 1, students will have a solid foundation in Python basics and the confidence to start creating their own simple programs.