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Since April 2021
Instructor since April 2021
Machine Learning and Generative AI in practice: from first model to RAG systems
From 47 $ /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.
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.
Extra information
Message me first with your current level, the kind of project you want to build and any deadline you are working towards. I will send back a short plan for the first few lessons.
What you need: a laptop, a stable connection, Zoom or Google Meet, and Python already installed. Some familiarity with Python is expected for this class. Free accounts on OpenAI or AWS are useful later but not required for the first lessons.
I am based in Berlin (CET) and regularly teach students in other time zones, so ask if you need an early or late slot.
What you need: a laptop, a stable connection, Zoom or Google Meet, and Python already installed. Some familiarity with Python is expected for this class. Free accounts on OpenAI or AWS are useful later but not required for the first lessons.
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.
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.
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.
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
60 minutes
The class is taught in
English
Urdu
Panjabi
Pashto
Availability of a typical week
(GMT -05:00)
New York
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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.
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.
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