From the course: Mistakes to Avoid in Machine Learning

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Assuming machine learning is the answer

Assuming machine learning is the answer

From the course: Mistakes to Avoid in Machine Learning

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Assuming machine learning is the answer

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- Machine learning is an exciting topic and it can be the most exciting project for a data scientist to be involved in. But I don't want you to be too eager to apply new techniques that you take on machine learning projects that simply won't yield real value. So I encourage you to assess whether a project warrants machine learning upfront. Here's some criteria you can use to determine if machine learning is actually necessary and likely to succeed for your use case. Run through these questions. Do you have a large and diverse set of data to start with? If your data source is small, you're unlikely to achieve meaningful results. How well defined is the problem you're trying to solve? Do you have a clear outcome that you were trying to predict and the hypothesis that you were testing against? Will a quick ad-hoc analysis suffice or do you need a full-fledged machine learning model? Often some straightforward descriptive…

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