Machine Learning Explainability
Kaggle Learn
At the center of this course are feature importance and model interpretation. Participants investigate why a predictive model behaves as it does. Its applied emphasis is communicating model drivers to people who need understandable results. The course is provided by Kaggle Learn and is available through its official learning website.
- Provider
- Kaggle Learn
- Resource type
- course
- Level
- Intermediate
- Duration
- Self-paced
- Price model
- Free
- Region
- United States
- Published
- 12 October 2026
- Last updated
- 12 October 2026
Data sources & provenance
Where this information came from, how it was checked and when it was last looked at. “Last updated” records a change to the record — it does not mean the record was verified.
- Data quality
- Data quality: Editorially Reviewed
- Primary source
- kaggle.com
- Sources cited
- 1
- Last reviewed
- 12 October 2026
- Last updated
- 12 October 2026
Spotted something out of date?
Anyone can suggest a correction — you don't need an account. An editor checks every suggestion against the sources before the record changes.
Related learning
course · Intermediate
LLM Course
This learning experience concentrates on transformers, tokenization and the Hugging Face model ecosystem. The lessons guide learners to use…
course · Intermediate
Context Course
The curriculum examines context engineering for AI coding agents, including skills, MCP, plugins and subagents, with emphasis on how the id…
course · Intermediate
Robotics Course
At the center of this course are the LeRobot toolkit and data-driven robotic learning. Participants work with robot datasets, policies and …
course · Intermediate
smol-course
Learners encounter post-training techniques for open language models as a connected set of skills. Working through the material, learners p…
Do you run AI training?
Add your course, certification or workshop so practitioners can find it in the graph.
Add a learning resource