Week 1 review
A summary of Week 1 of Experimental design for machine learning, covering types of problems, data collection, and data annotation
Well done on completing Week 1 of the course.
We hope you have found it useful so far and it has given you some ideas of what to think about when collecting data for use in machine learning projects.
In particular we focused on the following topics:
- types of machine learning and deep learning problems
- regression – counting or measuring things
- classification – what is the data showing?
- segmentation – which pixels are showing an object or area of interest?
- image collection
- camera settings such as focus and aperture
- types of camera
- file types
- attention to image backgrounds
- data annotation
- annotation tools
- outsourcing annotation
- active learning.
What’s next?
In this first week we have focused mostly on raw data acquisition and annotation. Next week we will look at ways to organise, supplement and maybe distribute your datasets to others.
This article is from the online course:
Experimental Design for Machine Learning
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