Machine Learning Foundations
Models, evaluation and the discipline that keeps you from fooling yourself
Created by Daniel Mercer
What you will learn
- Frame a business problem as a learning problem, or decide it is not one
- Build a validation split that does not leak
- Train and tune the workhorse models: linear, tree-based and ensembles
- Choose metrics that match the cost of each kind of mistake
- Engineer features without leaking the target
- Diagnose overfitting, drift and silent failure after deployment
Course content
14 sections · 172 lectures · 26h total length
- What this course covers, and what it does notPreview03:00
- How to get the most out of itPreview04:00
- Setting up and downloading the exercise files06:00
- Framing a learning problemPreview06:00
- Framing a learning problem in detail14:00
- A closer look at framing a learning problem10:00
- Worked example: framing a learning problem05:00
- Where framing a learning problem goes wrong11:00
- Exercise: framing a learning problem07:00
- Framing a learning problem in practice15:00
- Two approaches to framing a learning problem10:00
- Framing a learning problem — questions from previous students08:00
- Reviewing your work on framing a learning problem03:00
- Framing a learning problem — the edge cases05:00
- A second worked example: framing a learning problem10:00
- Applying framing a learning problem to your own project09:00
- When framing a learning problem is not enough13:00
Topics covered
Requirements
- Comfortable with Python and pandas
- Basic statistics — distributions and averages
- Linear algebra is helpful but not assumed
Description
The modelling part of machine learning is the easy part. This course gives equal weight to the harder part — building an evaluation you can trust — because a model that scores well on a leaky split will fail the moment it meets production.
Your instructor
ML engineer, ex-search infrastructure
Daniel spent years making search results better for millions of people, which mostly meant evaluation, not modelling. He teaches machine learning with the measurement work put back in.
Learner reviews
- 5 star73%
- 4 star22%
- 3 star3%
- 2 star1%
- 1 star1%
- QVQuentin V.2 March 2026
Good balance between theory and doing. I would have liked a couple more exercises, but that is a small complaint.
- AAAisha A.28 April 2026
The instructor answers questions properly rather than pointing you at the documentation.
- TDTariq D.28 February 2026
Very practical. I applied something from module four at work the same week.
- TRTariq R.7 July 2026
Exactly what I needed. It assumes you are an adult and does not over-explain.
- OLOlive L.2 June 2026
Good balance between theory and doing. I would have liked a couple more exercises, but that is a small complaint.
- YOYara O.1 December 2025
The instructor answers questions properly rather than pointing you at the documentation.
- BNBen N.7 January 2026
Very practical. I applied something from module four at work the same week.
- GGGrace G.25 February 2026
Genuinely well structured. I have started three courses on this topic and this is the first one I finished.
- PFPetra F.6 February 2026
Good balance between theory and doing. I would have liked a couple more exercises, but that is a small complaint.
Showing 9 of 5,602 reviews.
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