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
Decision Tree, Random Forest, Gradient Boosting
From one tree you can draw, to hundreds that win nearly every tabular competition.
- Level
- Intermediate
- Lessons
- 1
- Total time
- 22 min
- Simulations
- 2
Included in Introduction to Machine Learning · $9
Tools you'll use
Python
scikit-learn
pandas
NumPy
Skills
- Decision trees
- Random forest
- Gradient boosting
Try it now — free
This is the real simulation from the module. No account needed.
What you will be able to do
- Build a decision tree split by split
- Explain why ensembles beat a single tree
- Choose between random forest and gradient boosting
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 LearningFrom One Tree to a Forest
22 minOne tree is easy to read and easy to overfit. Two ways to combine many, and why they differ.
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