Your Team, Your Data, One Room
We run courses privately for your team, using your datasets and the problems your people actually work on. A public course teaches Python with someone else's data. A private one teaches it with yours, and the examples stay useful after everyone goes back to their desks.
We run up to twelve participants per trainer. That is small enough for a trainer to look at the code on your screen and tell you what is wrong with it, which is the part a video cannot do. Larger groups are no problem: we bring additional trainers rather than stretching one across the room, so the ratio holds however many people you send.
Any of our courses can run this way. We can also build something new: we start from what your team already knows and what they need to do, then design the course around the gap. If the subject sits outside our expertise, we find the experts and work with them.
Where a course needs groundwork first, we take on consulting to develop the material and understand the systems it has to cover.
Get a QuoteWhy Teams Book Privately
- Up to 12 per trainer: everyone gets looked at. Send a bigger group and we add trainers to keep the ratio, rather than filling a lecture hall.
- Your data: we teach with your datasets and code bases on request, so the worked examples are ones your team recognises.
- A team that learns together: shared reference points, and senior staff helping juniors while still picking things up themselves.
- Nothing to co-ordinate: one date, one invoice, no booking seats one at a time across a dozen calendars.
- Your schedule and place: at your office or online, split across days or weeks if that suits the team better.
Better Than a Team-Building Day
Put a team in a room for two days to solve real problems together and you get what an offsite is supposed to deliver, plus code that works on Monday. People who never talk shop discover they have been solving the same problem twice, in different ways.
Mixed experience levels help rather than hurt. Seniors explain what they know, which is how they find out how well they know it, and juniors ask the questions a course cannot anticipate. Everyone leaves with the same vocabulary and the same worked examples to point at in a code review.
It also works across teams. Analysts and engineers who share a course tend to stop handing each other work in formats the other has to undo.
How It Works
Tell Us What You Need
A short conversation about what your team does now, what you want them doing, and how much Python they already write.
We Design the Course
You get an outline to review before anything is booked, built from our material and yours. Change it as much as you like.
We Run It
At your office or online, on dates that suit you. Participants write code in every session; there are no slides to sit through.
Afterwards
Participants keep the course materials and notebooks, and can bring questions to our open office hours.
Popular Custom Training Topics
Machine Learning Foundations
Supervised and unsupervised learning, feature engineering, and model evaluation. The emphasis is on knowing which method fits a problem and why, so your team can rule options out quickly rather than trying everything scikit-learn offers and picking whatever scored highest on one split.
Deep Learning
Neural networks with PyTorch: computer vision, sequence models and transformers, built up from the training loop rather than assembled from copied snippets. Includes when a deep model is the wrong answer and something simpler will hold up better in production.
Building with LLMs
Retrieval-augmented generation, fine-tuning, and the engineering around a language model in a real application. Covers evaluating output that differs every run, and the failure modes that only appear once real users are asking real questions.
AI-Assisted Coding
Getting real work out of coding assistants, and reviewing what they produce. The skill that matters is judging generated code you did not write, quickly and honestly, so your team ships faster without quietly accumulating code nobody understands.
Putting Models Into Production
Deployment, monitoring and drift detection: the engineering that keeps a model working after the notebook is closed. Covers what to log, how to tell degradation from noise, and how to retrain without breaking what already depends on the model.
Evaluation and Model Risk
Measuring whether a model is actually any good, and where it fails. Aimed at teams who have to satisfy a reviewer, a regulator or an internal risk function, and need evidence that survives someone else reading it carefully.
Something Not Listed Here
This field changes faster than any course list, and the question your team has may not have had a name last year. We teach it at whatever depth you need, and where the expertise sits outside our own, we find the people who have it. Tell us the problem.
Every course here started as someone's specific problem.
Discuss Your RequirementsTalk to Us About Your Team
Tell us what your team works on and how many of them there are. We will come back with an outline and a fixed price, usually within two business days.
Request a Quote