Machine Learning Basics¶
These articles are designed to provide a first hands-on overview of basic Dataiku machine learning concepts so that you can easily create and evaluate your first models in Dataiku.
Tip
This content is also included in a free Dataiku Academy course Machine Learning Basics, which is part of the ML Practitioner learning path. Register for the course there if you’d like to track and validate your progress alongside concept videos, text summaries, hands-on tutorials, and quizzes.
Articles¶
- Concept: Preparing a Dataset for Machine Learning
- Concept: Quick Models
- Concept: Design Tab Overview
- Hands-On: Create the Model
- Concept: Result Tab Overview
- Concept: Model Summary Overview
- Hands-On: Evaluate the Model
- Concept: Feature Handling
- Concept: Review the Design
- Concept: Algorithms and Hyperparameters
- Hands-On: Tune the Model
- Concept: Explainable AI
- Concept: Partial Dependence
- Concept: Subpopulation Analysis
- Concept: Individual Explanations
- Concept Summary: Interactive Scoring
- Hands-On: Explain Your Model
- Wrap Up: Machine Learning Basics