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Trusted teacher
This teacher has a fast response time and rate, demonstrating a high quality of service to their students.
Since April 2021
Instructor since April 2021
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.
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.
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.
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.
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.
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.
Preparing for a data engineering, data science, machine learning or AI engineering interview? I have sat on both sides of that table. I am a Lead Data and Machine Learning Engineer in Berlin and I have been hired by, and interviewed for, Amazon, Delivery Hero, Goldman Sachs and Orion S.A. My open source guide for machine learning interviews has more than 700 stars on GitHub and is used by candidates worldwide.
WHO IT IS FOR
Candidates with onsite or final round interviews coming up for data or AI roles.
Engineers switching from backend, analytics or academia into data and ML.
People who keep reaching the last round and not getting the offer.
Students preparing for internships and new graduate roles.
WHAT WE COVER
Coding rounds: Python and SQL under time pressure, and how to talk while you code.
Data structures and algorithms, targeted at the patterns that actually come up.
SQL rounds: window functions, complex joins, debugging a slow query out loud.
Machine learning theory rounds: bias and variance, evaluation, regularisation, imbalanced data, and the follow up questions interviewers use to find the gaps.
Applied ML and case rounds: designing a model for a business problem end to end.
System design for data: pipelines, streaming, storage choices, trade offs.
GenAI and LLM interviews: RAG design, evaluation, cost, hallucination handling.
Behavioural rounds, including the Amazon leadership principles format, and how to build a story bank that does not sound rehearsed.
Salary and offer conversations.
HOW IT WORKS
We start by finding out which companies and which rounds you are facing, then work backwards from there. Most sessions are a realistic mock interview followed by direct feedback: what you did well, what an interviewer would have marked you down for, and exactly what to practise before the next session.
I give honest feedback. If you are not ready for a level, I will say so and we build a plan rather than pretending.
PRACTICAL DETAILS
Lessons are in English, online via webcam, and I work comfortably across time zones so we can match your interview schedule. Tell me your timeline when you message and I will prioritise accordingly.
WHO IT IS FOR
Candidates with onsite or final round interviews coming up for data or AI roles.
Engineers switching from backend, analytics or academia into data and ML.
People who keep reaching the last round and not getting the offer.
Students preparing for internships and new graduate roles.
WHAT WE COVER
Coding rounds: Python and SQL under time pressure, and how to talk while you code.
Data structures and algorithms, targeted at the patterns that actually come up.
SQL rounds: window functions, complex joins, debugging a slow query out loud.
Machine learning theory rounds: bias and variance, evaluation, regularisation, imbalanced data, and the follow up questions interviewers use to find the gaps.
Applied ML and case rounds: designing a model for a business problem end to end.
System design for data: pipelines, streaming, storage choices, trade offs.
GenAI and LLM interviews: RAG design, evaluation, cost, hallucination handling.
Behavioural rounds, including the Amazon leadership principles format, and how to build a story bank that does not sound rehearsed.
Salary and offer conversations.
HOW IT WORKS
We start by finding out which companies and which rounds you are facing, then work backwards from there. Most sessions are a realistic mock interview followed by direct feedback: what you did well, what an interviewer would have marked you down for, and exactly what to practise before the next session.
I give honest feedback. If you are not ready for a level, I will say so and we build a plan rather than pretending.
PRACTICAL DETAILS
Lessons are in English, online via webcam, and I work comfortably across time zones so we can match your interview schedule. Tell me your timeline when you message and I will prioritise accordingly.
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