Daniel Bourke is author of this piece about what to do if you have data and want to know how you can use machine learning with it.
For this article, you can consider machine learning the process of finding patterns in data to understand something more or to predict some kind of future event.
The following steps have a bias towards building something and seeing how it work:
- Problem definition – Rephrase your business problem as a machine learning problem
- Data – If machine learning is getting insights out of data, what data do you have?
- Evaluation – What defines success? Is a 95% accurate machine learning model good enough?
- Features – What features does your data have and which can you use to build your model?
- Modelling – Which model should you choose? How can you improve it? How do you compare it with other models? Experimentation – What else could we try? How do the other steps change based on what we’ve found? Does our deployed model do as we expected?
This is excellent source of information, with great and detailed schemes accompanying this long article, with clear and concise terminology and flow.
Stay tuned for more specific examples of each of the above steps and how they work in practice. Follow the author!
[Read More]