Module · Beginner
Getting Started
Three kinds of learning, and the two data distinctions that drive almost every later decision.
- 2 lessons
- 30 min
- 2 simulations
Included in Introduction to Machine Learning· $9
Explainable AI: Feature Importance, PDP, SHAP
Three tools answering three different questions. Using the wrong one gives a confidently wrong answer.
- Level
- Intermediate
- Lessons
- 1
- Total time
- 22 min
- Simulations
- 3
Included in Introduction to Machine Learning · $9
Tools you'll use
Python
scikit-learn
pandas
NumPy
Skills
- SHAP
- Permutation importance
- Partial dependence
Try it now — free
This is the real simulation from the module. No account needed.
What you will be able to do
- Explain an individual prediction with SHAP
- Measure feature importance with permutation importance
- Read a PDP without inferring causation
Syllabus
1 lesson · 22 minEvery lesson's title and summary is public. The lessons themselves open once you own the track.
Every lesson in Introduction to Machine LearningThree Questions, Three Tools
22 minWhich features matter? What shape is the effect? Why was this applicant rejected? Three questions, and no single tool answers all three.
Included with the track Simulation
Lifetime access
$9More in Introduction to Machine Learning
Module · Beginner
Dataset Splitting, Cross-Validation, Data Leakage
Before any algorithm. A bad split produces a beautiful number that lies.
- 2 lessons
- 34 min
- 2 simulations
Included in Introduction to Machine Learning· $9
Module · Beginner
Training, Evaluation, Bias–Variance, Tuning
How to train, how to measure with the right metric, and the one trade-off that explains nearly every model failure.
- 3 lessons
- 48 min
- 3 simulations
Included in Introduction to Machine Learning· $9
Module · Beginner
KNN — Classifier and Regressor
The algorithm that learns nothing, and teaches a great deal precisely because of that.
- 1 lesson
- 16 min
- 1 simulation
Included in Introduction to Machine Learning· $9