A live, interactive program that'll help you build production-ready machine learning systems from the ground up.
I'll lose my mind if I see another book or course teaching people the same basic ideas for the hundredth time. Most people are stuck in beginner mode, and finding help to solve real-world problems is hard.
I want to change that.
I started writing software 30 years ago. I've written pipelines and trained models for some of the largest companies in the world. I want to show you how to do the same.
This is the class I wish I had taken when I started.
This program will help you unlearn what you think machine learning is. It's a practical, hands-on class where you'll learn from years of experience and real-world examples.
When you join, you get lifetime access to the following:
And the best part is that you only pay once to join. There are no monthly fees. No annual fees. No hidden costs. You pay once to join and benefit forever until the end of time.
The program won't be easy. It'll take time and effort. But if you want to use machine learning to solve real-world problems, this is the class you don't want to miss.
This program is for software engineers, data scientists, data analysts, machine learning engineers, technical managers, and anyone anyone who wants to use machine learning to solve real-world problems.
Here are the criteria to succeed in the program:
Here is a summary of what makes this program unique:
Forget about theoretical concepts. This program will show you some of the things I've learned from real-life examples I've built during more than 30 years in the industry.
Learn from practical experience building machine learning systems that work in the real world.
Ask questions and interact with the instructor and other students in real time.
Step by step coding instructions to help you build a production system from scratch.
Here are the upcoming cohorts:
Live sessions will take place every Monday and Thursday at the same time. On Wednesdays, we'll host office hours when you can bring your questions projects or anything else you want to discuss.
Do not wait for a specific cohort to join the program. You have lifetime access to every past and future cohort, and the sooner you join, the more time you have to prepare.
Every session is recorded. You can attend live or watch the recorded version later.
Pay once to join and benefit from every past and future cohort. You can participate in as many iterations as you'd like. No restrictions.
Here are the contents of the six live sessions of the program:
If you are attending the live sessions, you should set aside a minimum of 4 hours every week during the three weeks of the program. This commitment will be enough for engineer leaders or anyone not interested in the coding portion of the program.
Those interested in implementing the concepts discussed in class should set aside 2 to 4 hours weekly to complete the code walkthroughs and work on the assignments.
Yes, we record every live session. You can decide when to attend classes live or catch up asynchronously later using the recording.
This program is not an introduction to machine learning.
While we'll discuss many fundamental ideas behind machine learning, beginners will find the sessions go much faster than what's optimal for them.
You only pay once to join the program and get immediate access to every past, present, and future cohort.
Every new iteration of the program is better than the ones before. Many students take classes once and then join a later cohort to benefit from the updates.
The lifetime access removes any pressure from having to complete the program when life gets in the way.
I'm a machine learning engineer with over two decades of experience building and scaling enterprise software and machine learning systems.
I love neural networks. I love to make them work at scale.
From 2009 to 2023, I built products for Disney, Boston Dynamics, IBM, Dell, G4S, Anheuser-Busch, and NextEra Energy, among other clients. I learned about trade-offs and how to create products that work.
I started this program in March 2023. Since then, thousands of students have graduated, and I can't wait to meet you in class.