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AI and machine learning · 30 lessons · up to 10 minutes each
Machine Learning Foundations
A planned foundation in framing tasks, fitting baselines, and checking model quality.
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Machine Learning Foundations
Permanent access to the lessons published in this track when you buy. Future lessons are not included. 0 of 30 lessons are published; 0 are in owner review and 30 are Coming Soon.
$29.0030 lessons still in progress
Beginner
Lessons 1–10
01 Machine Learning Foundations: Sketch problem framing and target
Topic: problem framing and target
Draw a small map of problem framing and target in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner4 minComing Soon02 Machine Learning Foundations: Name the parts of features and labels
Topic: features and labels
Label the main pieces in features and labels in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner5 minComing Soon03 Machine Learning Foundations: Trace an example of train and validation splits
Topic: train and validation splits
Follow one input through train and validation splits in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner6 minComing Soon04 Machine Learning Foundations: Compare choices for baseline selection
Topic: baseline selection
Write down one practical difference between baseline selection in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner7 minComing Soon05 Machine Learning Foundations: Find the boundary in fit and predict loop
Topic: fit and predict loop
Mark where fit and predict loop in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner8 minComing Soon06 Machine Learning Foundations: Inspect an example of classification metrics
Topic: classification metrics
Annotate a minimal example of classification metrics in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner9 minComing Soon07 Machine Learning Foundations: Connect regression metrics to a neighboring concept
Topic: regression metrics
Show how regression metrics in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner10 minComing Soon08 Machine Learning Foundations: Spot a failure mode in overfitting and regularization
Topic: overfitting and regularization
Describe one likely failure for overfitting and regularization in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner3 minComing Soon09 Machine Learning Foundations: Read an official example of data drift checks
Topic: data drift checks
Summarize an official example of data drift checks in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner5 minComing Soon10 Machine Learning Foundations: Choose a use case for responsible model review
Topic: responsible model review
Match one small use case to responsible model review in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Beginner6 minComing Soon
Intermediate
Lessons 11–20
11 Machine Learning Foundations: Build an example with problem framing and target
Topic: problem framing and target
Create a small working example that uses problem framing and target in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate4 minComing Soon12 Machine Learning Foundations: Compose features and labels with another component
Topic: features and labels
Combine features and labels in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate5 minComing Soon13 Machine Learning Foundations: Configure train and validation splits for a small case
Topic: train and validation splits
Set up train and validation splits in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate6 minComing Soon14 Machine Learning Foundations: Validate inputs around baseline selection
Topic: baseline selection
Add a check around baseline selection in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate7 minComing Soon15 Machine Learning Foundations: Use fit and predict loop in a short workflow
Topic: fit and predict loop
Add fit and predict loop in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate8 minComing Soon16 Machine Learning Foundations: Refactor classification metrics into a reusable unit
Topic: classification metrics
Extract classification metrics in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate9 minComing Soon17 Machine Learning Foundations: Exercise regression metrics with sample data
Topic: regression metrics
Run sample data through regression metrics in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate10 minComing Soon18 Machine Learning Foundations: Observe overfitting and regularization outputs
Topic: overfitting and regularization
Capture the result of overfitting and regularization in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate3 minComing Soon19 Machine Learning Foundations: Recover from a data drift checks failure
Topic: data drift checks
Handle one expected failure from data drift checks in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate5 minComing Soon20 Machine Learning Foundations: Document responsible model review behavior
Topic: responsible model review
Write a short usage note for responsible model review in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Intermediate6 minComing Soon
Advanced
Lessons 21–30
21 Machine Learning Foundations: Test a boundary around problem framing and target
Topic: problem framing and target
Add an edge-case test for problem framing and target in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced4 minComing Soon22 Machine Learning Foundations: Harden features and labels
Topic: features and labels
Add the relevant access, safety, or validation control to features and labels in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced5 minComing Soon23 Machine Learning Foundations: Measure train and validation splits
Topic: train and validation splits
Record a small baseline for train and validation splits in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced6 minComing Soon24 Machine Learning Foundations: Reduce cost in baseline selection
Topic: baseline selection
Change one measurable cost in baseline selection in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced7 minComing Soon25 Machine Learning Foundations: Diagnose a fit and predict loop failure
Topic: fit and predict loop
Use an observable signal to explain a failure in fit and predict loop in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced8 minComing Soon26 Machine Learning Foundations: Make classification metrics repeatable
Topic: classification metrics
Show that rerunning classification metrics in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced9 minComing Soon27 Machine Learning Foundations: Review trade-offs in regression metrics
Topic: regression metrics
Compare two documented approaches to regression metrics in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced10 minComing Soon28 Machine Learning Foundations: Improve overfitting and regularization maintainability
Topic: overfitting and regularization
Refine overfitting and regularization in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced3 minComing Soon29 Machine Learning Foundations: Verify data drift checks end to end
Topic: data drift checks
Check that a complete small flow using data drift checks in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced5 minComing Soon30 Machine Learning Foundations: Audit responsible model review against its source docs
Topic: responsible model review
Check the implementation of responsible model review in the context of supervised machine learning experiments and evaluation; save a short example or check that demonstrates the result.
Advanced6 minComing Soon
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