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

From 35 £ /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

36 £ /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

36 £ /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.

4.85

(13)

Hello, I am a doctoral student in electrical engineering and associate professor in engineering sciences, experienced in the field of electrical engineering, I offer support courses in the subjects of engineering sciences (Electronics, automatics, electrical engineering, automation, programming).

Digital electronics
Analog electronic
electromagnetism (propagation of high frequency waves)
Automatic (continuous, sampled)
electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
renewable energy (wind, PV)
engineering Sciences
RDM
Python,VHDL
PIC Microprocessor and Microcontroller
Signal processing and data acquisition
Engineering Sciences

These courses allow the student to get up to speed and regain confidence in all scientific subjects, just as they prepare him effectively for the Baccalaureate, the Preparatory Classes or various examinations of the engineering classes.

COURSE OBJECTIVES AND PEDAGOGICAL APPROACH

Resumption and deepening of fundamental concepts through exercises with course reminders.

Put the student in a situation of questioning and research.

Respond to individual issues and questions

Exercise training in order to achieve real mastery of the content.

Learn to build theoretical reasoning from observable facts or hypotheses.

Specific preparation for higher education requirements (in-depth content, increase in work capacity, enrichment of scientific background)

This educational approach is effective since it has often led me to interesting results with my students.

Associate professor provides support courses in electrical engineering

Matías

4.85

(13)

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.

Fatima

4.85

(13)

Give your child the future he deserves.

these courses are intended to help your child succeed in significantly increasing the average by assimilating the course and preparing for the next tests (assimilating the course, practicing standard exercises, anticipating the teacher's expectations and test questions, have writing methods) in the following subjects:
Maths - Physics-Chemistry and Computer Science.

Enrique

4.85

(13)

Don't settle for anything less than excellence.
I am an Aerospace Engineer with a Master's degree in Quantum Physics and have completed Ph.D. work at the University of Cambridge in Computational Physics. Additionally, I have 4 years of experience developing MATLAB and possess deep programming skills in MATLAB/Simulink family, C/C++, Fortran, and Python.

With over 12 years of tutoring experience, I have successfully guided more than 50 students worldwide to achieve distinction in various fields. Consistent results are my priority, and I strive for excellence in all aspects of my teaching.

My lessons are customized to meet each student's unique needs and are designed to be engaging and insightful.

Whether you are at a school level or require advanced or professional-level instruction, I offer support in the following areas:
- Preparation for IB/IA, A-Levels, GCSE, University Entry, or equivalent.
- Experience in preparing students to access world-class schools and universities, including Cambridge University, Oxford, Ivy League and other top institutions in the UK and US.
- University levels (undergraduate and postgraduate).
- High school studies and diploma programs.
- Assistance with specific projects at a professional level, including job interview preparation.
- Extensive experience working with children.

Every lesson is meticulously planned in advance to ensure that it aligns with your goals and targets areas for improvement.
I prioritize a dynamic and interactive learning experience, with one-on-one sessions tailored to your individual requirements. Lessons will be conducted via webcam, enabling you to connect from anywhere.

I have a highly flexible schedule and can adapt to accommodate your needs.
If you have any questions about my teaching method, availability, or pricing, please don't hesitate to reach out. I am here to assist you and provide the support you need.

Zakaria

4.85

(13)

* This digital training aims to introduce you to the Scratch tool and through the game world, and gradually, to discover programming concepts such as loops, conditions or variables. It is aimed at anyone who is new to Scratch and who wants to create games and animations.

* Learning programming will allow students to develop their skills and will certainly allow them to meet the expectations of the future working world and emerging careers.

* In addition, learning programming allows the development of algebraic, algorithmic and computational thinking. Programming also helps to improve and develop students' sequencing ability, as well as their communication skills. Thus, there are several advantages to teaching programming, but the important thing is to remember that this learning teaches students that digital is not only for entertainment, but that it is possible to become creators. active and creative content.

Gaëlle

4.85

(13)

While adults are still debating whether kids should use AI, they are already using it.
The question isn't "should they?" it's "how do we do it intelligently?"

In this course, your child will discover:
✓ What AI actually is: not magic, not mystery. How machines think, what they can do, what they can't
✓ How ChatGPT really works: not just "ask a question and get an answer," but why it responds that way, where it fails, when to trust it
✓ What LLMs are (Large Language Models): in language they understand, not tech jargon
✓ Create with AI: custom avatars, interactive stories, real projects using real tools
✓ Think critically about AI: Bias, privacy, creativity. What does AI do better than humans? What can't it do?
✓ Real-world applications: How AI transforms medicine, education, art, gaming, everyday life

Why this is different:
Most AI courses for kids teach "here's the tool, use it." I teach how to think about AI.
Your child will learn to see AI not as black magic or a solution to everything, but as a powerful tool with real limits.
And, more importantly: that they can control how they use it.

What they take home:
Real projects they created (custom avatar, interactive app, analysis of a real AI case study). A genuine understanding of how it works. And the ability to use AI responsibly and creatively.

Format: Online | 60–90 min sessions | Flexible, adapted to their age and pace

For curious kids asking "How does ChatGPT actually know things?"

Giuliano

4.85

(13)

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

4.85

(13)

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.

Azzedine

4.85

(13)

Become a Trading Pro with NinjaTrader!

Are you passionate about trading and want to master one of the most powerful trading platforms on the market? If so, this ad is for you!

As a seasoned NinjaTrader expert, I offer personalized courses to help you master this exceptional trading platform. Whether you want to learn how to trade manually or automate your strategies using C# and custom indicators, I'm here to guide you every step of the way.

My courses are designed for traders of all levels, from complete beginners to experienced professionals. Here's what you can expect:

- In-depth training on using NinjaTrader, including navigating the interface, executing orders, and analyzing the markets.
- Detailed instructions on creating and optimizing automated trading strategies using C#.
- Practical advice on using custom indicators to refine your technical analysis and make informed trading decisions.

Whether you are looking to generate additional income through trading or make trading your full-time career, my courses will provide you with the knowledge and skills needed to succeed in the financial markets.

Don't miss this opportunity to become a true trading expert with NinjaTrader! Contact me today to reserve your spot and begin your journey to financial success.

Hassane

4.85

(13)

Hello,

My name is Hassane, and I've been passionate about computers for over 20 years. With two decades of teaching experience, I've had the privilege of supporting learners of all ages and levels in developing their computer skills and achieving their professional and personal goals.

Computer science is an essential skill today, opening the door to countless opportunities. Whether you want to learn programming, website design, data analysis, or complex problem-solving, I'm here to guide you every step of the way.

What I propose:
In my classes, we explore a wide range of topics to meet the needs of both beginners and advanced learners:

Computer fundamentals: master the basics to get started, Hardware, Software, Binary, Operating system.
Programming: learn to code efficiently in different languages (C#, Python, VBA).
Web development: creating modern websites and applications.
Databases: understanding, managing and analyzing data, UML, MERISE.
Project management: Ms-Project, Agil, Scrum, Kanban

Teaching methods:
I adopt a dynamic and interactive approach to ensure a rich and enjoyable learning experience:

Interactive courses: clear explanations adapted to your pace.
Practical exercises: to immediately apply the concepts learned.
Collaborative projects: developing real solutions as a team.
Personalized monitoring: answer your questions and support your progress.
Why choose me?
20 years of experience in computer teaching.
Proven methods suitable for all levels.
Personalized support to help you achieve your goals.
A passion for passing on skills that make a difference.
Whether you are a student, a professional looking to retrain, or simply curious, my courses will provide you with the tools you need to succeed in this rapidly evolving field.

Join me today!
Please contact me to learn more or to discuss your specific needs. Together, let's build your digital future.

Hassane
Experienced computer teacher and trainer

Meriem

4.85

(13)

Python is today one of the most widely used programming languages in the world, both in Data Science, Artificial Intelligence, Web Development and for task automation.
In this course, I will guide you step by step according to your level:

Beginner: basics of the language (variables, loops, conditions, functions).

Intermediate: data manipulation (Pandas, NumPy), file management, object-oriented programming.

Advanced: practical projects (data analysis, machine learning, automation, API, web scraping).

My goal is to make learning clear, practical, and motivating. You'll not only learn how to code in Python, but also how to structure your projects and apply your knowledge to real-life scenarios.

Farouk

4.85

(13)

These courses are part of a structured and progressive training in Object-Oriented Programming (OOP) with JavaScript, designed for beginner or intermediate developers who want to understand in depth how the language works, write clearer, more maintainable code and prepare themselves calmly for modern frameworks like React ⚛️.

Object-Oriented Programming is often perceived as complex or abstract.

My goal is simple: to make it logical, concrete, and immediately applicable.

🎯 Training Objectives

Upon completion of this training, you will be able to:

Understanding what Object-Oriented Programming really is (and when to use it)
Create and manipulate objects in JavaScript in a clean and efficient way
Use ES6 classes, constructors, and methods with confidence
Mastering this, the prototype, and the instantiation logic
Apply encapsulation, inheritance, and polymorphism without confusion
Avoiding common mistakes made by OOP beginners
Structure your JavaScript code like a professional developer

📖 Training Plan – Object-Oriented Programming in JavaScript
1. Introduction to Object-Oriented Programming 🧠
Understanding the concept, objectives and benefits of OOP.
2. Procedural Programming vs. OOP
Why unstructured code quickly becomes unmanageable.
3. Objects in JavaScript
Properties, methods and representation of the real world.
4. The keyword this
Understanding the execution context (often poorly understood).
5. Limitations of simple objects
Why duplicating code is a bad idea.
6. Constructive functions
Create multiple objects from the same model.
7. The keyword new
What it's actually doing under the hood.
8. The prototype
Sharing methods and memory optimization.
9. ES6 Classes
Modern syntax and best practices.
10. The builder
Proper initialization of objects.
11. Data Encapsulation
Protect the internal state of objects.
12. Inheritance between classes
Reusing code intelligently.
13. The keyword super
Communication between parent and child in the classroom.
14. Polymorphism
The same behavior, several forms.
15. Composition vs. Inheritance
Choosing the right architecture.
16. Best practices in OOP
Write readable, scalable, and maintainable code.
17. Common mistakes made by beginners
Pitfalls to absolutely avoid.
18. Guided practical exercise
Creation of a concrete class (product, user, etc.).
19. Assessment Quiz (Multiple Choice Questions)
To validate the actual understanding of the concepts.

🛠️ Teaching method: Understand before writing

This training program is based on a progressive and pragmatic approach:
Clear and illustrated explanations
Concrete examples from real projects
Simple but effective exercises
Constant questioning to avoid rote learning
Adaptation to the learner's level and pace
Here, we don't "recite OOP" — we understand it.

🚀 Learner's result

At the end of the training, you will not only know how to write a JavaScript class.
You will know:

1- Why does it exist?
2- When to use it
3- and when not to use it

You will leave with:
a solid understanding of OOP
a cleaner and more professional code
an ideal foundation for learning React, Node.js or any other modern framework

Ammar

4.85

(13)

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.

Baia

4.85

(13)

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

Arnaud

4.85

(13)

I work as a data consultant and hold a master's degree in Data Science and Statistics from UCLouvain. I use Python and data manipulation and analysis tools daily in a professional context.

I offer Python and data analysis courses for students, beginners and people wishing to develop practical skills in programming and data.

The lessons are tailored to your level and your goal. We can work on, among other things:

* the basics of Python and programming logic;
* variables, conditions, loops and functions;
* Data manipulation with Python and pandas;
* data cleaning and transformation;
* data exploration and visualization;
* applied statistics;
* solving practical exercises and projects;
* Understanding and debugging your code.

I prefer a practical approach: we start with a concrete problem or dataset, gradually build the solution, and analyze the errors encountered together.

The goal is to understand what you are doing rather than simply reproducing code, in order to gradually become self-reliant.

I can also support a project or student work by explaining the approach, the code and the methods used, without doing the work for you.