Curriculum
Workshops and Courses.
Most technical training teaches you to follow along. These courses teach you to make decisions: how to break a problem down, choose an approach, and tell whether it is working.
I have spent over a decade building software and machine learning systems, and I lead engineering teams shipping AI to production. I teach at Omaha Data Science Academy. Everything here comes out of systems that shipped, with messy data, hard deadlines, and users who noticed when something broke.
Workshops for teams
Built for the room.
I build workshops and technical talks for engineering organizations, user groups, and conferences.
Topics include applied AI, agentic systems, Model Context Protocol, AI-ready repositories, evaluation, MLOps, Python development, and machine learning architecture.
Sessions run from a 30-minute talk to a 90-minute hands-on lab. I build each one for the room. A session for a platform team already running models in production looks nothing like an introduction for analysts, and both are worth doing well.
Tell me who is in the room, what your timeline is, and what you want people to be able to do afterward.
Book a workshop →Courses
Cohort courses.
Python fundamentals, then applied machine learning, then the decisions that get harder once a model has to hold up.
- Intermediate Prerequisite: Python fundamentals
Machine Learning I
Go from working with data to building models that learn from it.
This course covers the decisions behind a machine learning project: how to frame the problem, prepare the data, choose an approach, and judge whether the result is any good. You will build models. The harder skill is choosing which one to build.
Ask about the next cohort →What we cover
- The workflow: problem framing, features, labels, training data, validation, and testing.
- Building models: supervised learning, regression, classification, and feature engineering in Python.
- Evaluating what works: metrics, overfitting, underfitting, and why a good score does not always mean you have a good model.
- Intermediate to advanced Prerequisite: Machine Learning I or equivalent
Machine Learning II
Building a model is the easy part.
This course is about what happens after a promising experiment leaves the notebook. Stronger algorithms are part of it. The larger part is rigor: designing experiments that tell you something, diagnosing why a model behaves as it does, and deciding whether it is ready for anyone to depend on.
Ask about the next cohort →What we cover
- Advanced modeling: ensemble methods, model selection, and hyperparameter tuning.
- Better experimentation: cross-validation, error analysis, and systematic approaches to improvement.
- In production: interpretability, bias, data quality, changing data, and operational constraints.
- Beginner No prerequisite
Python Programming
Build a foundation you can keep building on.
You learn Python by writing programs. By the end you can take a problem you have never seen, break it into pieces, and get something working.
Ask about the next cohort →What we cover
- Fundamentals: variables, data types, conditionals, loops, and functions.
- Working with data: lists, dictionaries, sets, files, and the patterns Python developers reach for.
- Writing programs other people can read: code organization, debugging, reusable functions, and the habits that keep software maintainable.
Get in touch.
For team workshops, include the size and experience level of the group and the date you have in mind.
Start the conversation →