Python, SQL and Machine Learning with a Lead Data & AI Engineer (ex Amazon)

From 39 € /h

Learn Python, SQL, machine learning and AI from someone who builds these systems in production every day. I am a Lead Data and Machine Learning Engineer based in Berlin, with 9 years at Amazon, Delivery Hero, Goldman Sachs and now Orion S.A., and I have been teaching here since 2021.

WHO THIS CLASS IS FOR

Complete beginners who want to learn programming properly, from zero, with no assumptions.
University and bootcamp students stuck on Python, SQL, algorithms or a machine learning assignment.
Working professionals moving into data engineering, data science or AI roles.
Candidates preparing for technical interviews at product and tech companies.

WHAT WE CAN COVER

We pick the path together based on your goal. Options include:

Python fundamentals, clean code and object oriented programming
Data structures and algorithms, with the reasoning behind each choice
SQL and databases, from joins and window functions to designing a warehouse
Pandas, NumPy, data cleaning and exploratory analysis
Machine learning: regression, classification, trees, model evaluation, and the quiet mistakes that ruin a model
Deep learning and natural language processing foundations
Generative AI in practice: RAG, embeddings, vector databases, LangChain, OpenAI and AWS Bedrock
Data engineering on AWS: S3, Glue, Redshift, SageMaker, Airflow, Spark and dbt
Interview preparation, including mock interviews and system design for data roles

HOW LESSONS WORK

In the first lesson we work out where you are and what you actually want to reach, and I map a path to it. After that every session is hands on. You write code, I review it live, and I explain the reasoning rather than just handing you the answer.

You finish each lesson with something that works: a script, a notebook, a query, a pipeline, or an answer you could confidently give in an interview. Between lessons you get exercises and a project that grows week by week, so you end up with something real to show an employer.

WHY LEARN WITH ME

I teach what I do. The examples come from real systems that serve real users, not from textbook datasets. I will also tell you honestly when an approach will not hold up in production, which is the main reason to learn from a practitioner rather than a video course.

My open source guide for machine learning interviews has more than 700 stars on GitHub and is used by candidates preparing for data and ML roles.

PRACTICAL DETAILS

Lessons are in English, online via webcam, and I am comfortable working across time zones. Suitable for teenagers from 14 and for adults. No prior experience is needed for the beginner track.

Extra information

Before we start, message me with your goal, your current level and any deadline you are working towards. I will reply with a short plan for the first few lessons so you know what you are booking.

What you need: a laptop with a stable internet connection, and Zoom or Google Meet. We share a screen and a code editor during the lesson. If your Python setup is not ready, we set it up together in the first session.

Lessons are taught in English. Beginners are welcome with no prior experience. If you already work in tech and want to go deep on machine learning, GenAI or data engineering, say so and we skip the fundamentals.

I am based in Berlin (CET) and regularly teach students in other time zones, so ask if you need an early or late slot.

Location

Online from Germany

About Me

I am a Lead Data and Machine Learning Engineer based in Berlin, with over 9 years of building production data and AI systems at Amazon, Delivery Hero, Goldman Sachs and, currently, Orion S.A. I have been teaching on Apprentus since 2021 alongside that work, and I teach exactly what I do every day rather than theory I read about once.

My students are usually one of four people: a complete beginner who wants to learn programming properly from zero, a university or bootcamp student whose Python, SQL or machine learning assignment has stopped making sense, a working professional moving into data engineering, data science or AI, or a candidate preparing for technical interviews. I enjoy all four, and I adapt completely to which one you are.

How I teach: we start with a short conversation about where you are now and what you actually want to reach. I do not hand out a fixed syllabus before I know that. Every lesson is hands on. You write code while I watch, I review it live, and I explain the reasoning behind the fix instead of just giving you the fix. That is the part that makes it stick.

You finish each session with something concrete: a working script, a clean notebook, a pipeline that runs, or an answer you could confidently give in an interview. Between lessons you get exercises and a project that grows week by week, so after a few months you have something real to show an employer.

I am patient with beginners who are starting from nothing, and direct with people who want to be pushed hard. If something you are doing would not survive in a real production system, I will say so, because that honesty is the reason to learn from a practitioner instead of a video course.

Lessons are in English, online via webcam, and I am used to working across time zones. My open source guide for machine learning interviews has more than 700 stars on GitHub, and I use the same material with students preparing for interviews.

Education

MSc in Computer Science, Concordia University Ann Arbor, United States (2021 to 2023), with a focus on machine learning and data systems.

BSc in Computer Science, Government College University Lahore, Pakistan (2013 to 2017), covering algorithms, databases and software engineering.

I also keep learning continuously through AWS, Google Cloud, Databricks and Terraform certification tracks.

Experience / Qualifications

WORK EXPERIENCE (9+ years)
Lead Data and Machine Learning Engineer, Orion S.A., Berlin (2024 to present)
Senior Data Engineer, Delivery Hero, Berlin
Senior Data Engineer, Amazon
Data Engineering consultant (contract), Goldman Sachs, London (2021)
Earlier roles at NorthBay Solutions (AWS Advanced Consulting Partner) and Teradata

WHAT I WORK WITH DAILY
Python, SQL, Spark, Kafka, Airflow, dbt, Databricks, Terraform
AWS (SageMaker, Bedrock, Glue, Redshift, S3), Google Cloud and BigQuery
Machine learning, deep learning, natural language processing
Generative AI: RAG, LangChain, vector databases, OpenAI and AWS Bedrock

CERTIFICATIONS
AWS Certified Machine Learning Specialty
AWS Certified Data Engineer Associate
AWS Certified Solutions Architect Associate
AWS Certified Developer Associate
AWS Certified Cloud Practitioner
Google Cloud certified
HashiCorp Terraform Associate
Databricks certified
Deep Learning, Machine Learning and Statistics specializations

OPEN SOURCE
My machine learning interview guide on GitHub has more than 700 stars and is used by candidates preparing for data and ML interviews.

TEACHING
Teaching on Apprentus since April 2021.

Age

Teenagers (13-17 years old)

Adults (18-64 years old)

Student level

Beginner

Intermediate

Advanced

Duration

30 minutes

45 minutes

60 minutes

The class is taught in

English

Urdu

Panjabi

Pashto

Skills

Computer programming

Database

Python

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

Machine Learning and Generative AI in practice: from first model to RAG systems

41 € /h

This class is for people who already write some Python and now want to build machine learning and generative AI systems that work outside a notebook. I am a Lead Data and Machine Learning Engineer in Berlin with 9 years at Amazon, Delivery Hero, Goldman Sachs and Orion S.A., and I teach the same methods I use at work.

WHO IT IS FOR

Developers and analysts moving into machine learning or AI roles.
Students who understand the theory but have never shipped a model.
Founders and product people who want to build a real AI feature rather than a demo.
Anyone preparing for machine learning or AI engineering interviews.

WHAT WE COVER

Framing a problem: what is predictable, what is not, and how to tell before you waste a month.
Core machine learning: regression, classification, tree based models, feature engineering.
Model evaluation done properly: leakage, class imbalance, baselines, and why your accuracy score is lying to you.
Deep learning and natural language processing foundations.
Large language models in practice: prompting, structured output, evaluation and cost control.
Retrieval augmented generation end to end: chunking, embeddings, vector databases, reranking, and measuring whether the answers are actually correct.
Agents and tool use with LangChain and LangGraph.
Deployment: turning a model or an AI feature into a service, plus monitoring and drift.

HOW IT WORKS

We agree on a target project in the first lesson and build towards it. You write the code, I review it live and explain the reasoning. You leave with a working system in your own repository rather than a folder of tutorials.

I will tell you honestly when an approach will not survive real traffic or real data. That is the main reason to learn this from someone who runs it in production.

PRACTICAL DETAILS

Lessons are in English, online via webcam, and I work comfortably across time zones. You need working Python basics for this class. If you are starting from zero, book my Python, SQL and Machine Learning class instead and we build up to this one.

Data Engineering on AWS: build pipelines, lakehouses and streaming systems

41 € /h

Learn how data actually moves through a modern company: ingestion, storage, transformation, orchestration and serving. I am a Lead Data and Machine Learning Engineer in Berlin with 9 years of building these systems at Amazon, Delivery Hero, Goldman Sachs and Orion S.A. This class teaches the same architecture and tooling I use at work.

WHO IT IS FOR

Analysts and backend developers moving into data engineering.
Data scientists who keep getting blocked by broken or missing pipelines.
Students and career switchers who want a portfolio project that looks like real production work.
Engineers preparing for data engineering interviews or AWS certifications.

WHAT WE COVER

SQL that holds up under pressure: joins, window functions, query plans and performance.
Data modelling: star schemas, slowly changing dimensions, and how to design tables people can actually query.
Python for data engineering: clean, testable transformation code.
Batch pipelines with Airflow and dbt, including testing and data quality checks.
Spark for large datasets, and when you genuinely do not need it.
Streaming with Kafka and Kinesis: events, ordering, exactly once, and the traps.
AWS in depth: S3, Glue, Redshift, Athena, Lambda and IAM, plus cost control.
Lakehouse patterns with Databricks and Delta or Iceberg.
Infrastructure as code with Terraform, and CI CD for data.
Monitoring, alerting and what to do at 3am when a pipeline fails.

HOW IT WORKS

We pick a realistic project in the first lesson, for example a pipeline that ingests raw events and serves a clean analytics table, and we build it over the following sessions. You write the code, I review it live and explain the trade offs. You finish with a repository you can show in an interview.

We can also work on your own company's problems if you bring them, as long as you can share enough of the shape of the data.

PRACTICAL DETAILS

Lessons are in English, online via webcam, and I work comfortably across time zones. You should already be able to write basic Python and SQL. If you are not there yet, start with my Python, SQL and Machine Learning class and we build up to this one.

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.

Adam

4.88

(85)

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.

Programming Languages: Python, C/C++, C# and Java.

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.

Amin

4.88

(85)

Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance.
Whether you've never written a line of code or you're a student who needs to pass a programming course, this is a practical, no-fluff introduction that gets you writing real code from session one.
What we can cover depending on your goals:

Python fundamentals: variables, loops, functions, data structures
- Object-oriented programming (OOP)
- Data manipulation with pandas and NumPy
- Introduction to machine learning with scikit-learn
- Database management with SQL
- C and Java upon request
- MATLAB and R available for engineering/science students

Why learn with me?
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.

Giuliano

4.88

(85)

Python is a powerful and versatile programming language with countless possibilities. You can use it for data analysis, image processing, automation, software development, hardware control, and much more.

Do you want to create your own software?
Work with data or images?
Automate repetitive tasks?
Control or manage your own hardware?

Whether you are just starting to learn Python or already have a specific project and need some guidance, I would be happy to help you.

My goal is to explain things clearly, adapt to your level, and help you understand not only how to make something work, but also why it works.

Let's turn your ideas into working Python projects!

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.

Baia

4.88

(85)

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.

Yomna

4.88

(85)

This course is designed to introduce students aged 7 to 16 to the world of programming through two of the most widely used and industry-relevant languages: C++ and Python.

The class provides a structured, age-appropriate pathway into programming, whether the student is a complete beginner or already exploring coding through platforms like Scratch or Code.org. Emphasis is placed on understanding logic, building problem-solving skills, and writing real code in a supportive, project-based environment.

Taught by an engineering student with hands-on experience in both C++ and Python, this course empowers students to explore the power of code and build a strong foundation in computational thinking — essential for future studies in engineering, robotics, AI, or game development.

Amr

4.88

(85)

Private Programming Lessons for you / your family / your company employees
Programming Tutor – IGCSE & Computer Science Subjects
Deeper understanding, stronger results

• Lecturer at the American University AUC
• Over 20 years of experience in training students for government employees, oil companies (BP), food companies (Nestle), banks (CIB), and telecommunications companies (Vodafone).

• Teaching curricula, syllabuses, courses:
o IGCSE (Computer Science 0478, ICT 0417)
o Programming and computer courses for all educational levels (from primary to university)
o Microsoft Windows, Word, Excel, PowerPoint, Outlook, MS-Project
o Programming, C, C++, VB.NET, C#, Python, Database, SQL, MQL, VBA
o HTML, CSS, JavaScript, Angular
o Different database systems
o Data analysis using Excel
o Computer and Information Colleges Curricula
o Using artificial intelligence in life and work

• Master office applications to improve your job performance.
• Prepare yourself to work as a Front-End / Back-End / Full Stack Developer
• Theoretical and practical training for market requirements
• Don't miss out on technology. Lessons are designed for the elderly, in a simple and understandable way (use of computers and their programs, use of mobile phones, dealing with the Internet and social media).
• Lessons are available in person or online.

Ali

4.88

(85)

I teach Python, C and C++ one to one, online or in person around Birmingham.

Most of my students fall into one of three groups. Some are at GCSE or A-Level and need to get comfortable with a language before an exam or a coursework deadline. Some are at university, usually on an engineering or computing degree, and have hit something specific that isn't clicking: pointers, memory, recursion, object orientation, or a project that won't compile. And some are adults starting from nothing, often because work has started asking them to automate things.

Lessons are built around code you can run. I'll ask what you're working on and where you got stuck, then we write something small together, break it on purpose, and work out what the error message is actually telling you. Reading error messages properly is half of programming and almost nobody teaches it.

Areas I cover regularly:

Python from the basics through functions, data structures, file handling, object orientation and libraries like NumPy and Pandas
C and C++, including the parts that cause most of the trouble: pointers, memory management, structs, classes and compilation
GCSE and A-Level Computer Science across all exam boards, including pseudocode, trace tables and written paper technique
A-Level NEA projects and university coursework, plus debugging sessions and code review
Embedded C for Arduino, ESP32 and microcontroller projects, which is the work I do professionally

After each lesson I send written notes covering what we did, worked through step by step, so you have something to revise from later rather than trying to remember what was on screen.

First session is free and lasts 30 minutes. We use it to work out what you need and whether I'm the right person for it. If I'm not, I'll say so and point you somewhere better.

Message me with what you're studying and what's giving you trouble, and I'll tell you honestly how I'd approach it.

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

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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.

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.

Manahil

4.88

(85)

Master Computer Science & Coding Concepts Easily!

Computer Science doesn't have to be complicated. I focus on simplifying complex logic, algorithms, and practical programming so you can build strong foundational knowledge.

What You Will Learn:

Python Fundamentals: Data types, loops, logic, and object-oriented programming (OOP).

Data Structures & Algorithms: Practical logic building and problem-solving techniques.

Database & SQL: Basics of designing relational databases and writing queries.

Data Analysis Tools: Introduction to Python libraries like NumPy and Pandas for real-world applications.

Teaching Approach:

Interactive live sessions with code-along exercises.

Step-by-step breakdown of academic homework and practical assignments.

Patient, structured, and student-centric support.

Feel free to send a message or book a lesson to get started on your tech journey!

Ivana

4.88

(85)

I am an experienced computer science teacher with many years of teaching experience and a university degree in Mathematics and Computer Science.

I offer individual online lessons in programming and computer science for school students, as well as support for university students in selected subjects. Lessons can cover Python, MATLAB, SQL and databases, algorithms and programming fundamentals, computer systems, and web development with HTML, CSS and JavaScript.

My lessons are adapted to each student's previous knowledge, current curriculum and individual goals. I explain concepts step by step and focus on understanding the logic behind programming rather than simply memorizing code.

We can work on current school or university topics, programming exercises and assignments, fill gaps in knowledge, prepare for tests and exams, or develop practical programming skills.

Lessons are taught online in Serbian, Bosnian or Croatian, which can be particularly helpful for students from families from the former Yugoslavia who live and study in Germany, Austria, Switzerland or other countries.

Kate

4.88

(85)

Learn to code by creating your own games and interactive projects! These online lessons are designed for children and teenagers aged 7–17, from complete beginners to students with some coding experience.

We choose Scratch, Python, or Roblox Studio based on your child’s age, interests, and level. Students learn programming concepts, practise logical thinking, and discover how to find and fix errors independently.

I’ve been teaching since 2018 and have five years of software development experience. Each lesson combines clear explanations with practical activities in a friendly environment where questions are always welcome.

Students also get access to my learning platform to review materials and practise between lessons.