Lesson Title
- Defining your problem clearly is a critical step in any ML project.
A poorly defined question can lead to poor or even meaningless
results.
- Machine learning is best suited for tasks that play to computational
strengths (e.g., pattern recognition, scale, consistency).
- A good ML problem is clear, measurable, feasible, appropriate for
ML, and ethically sound.
- Developing an understanding of the shared vocabulary is essential
for clearly categorising and efficiently discussing your problem
type.
- The context of your data is crucial for understanding the
limitations of your analysis and the conclusions you can reliably
draw.
