If you want consistent results, you need a consistent process.
Here is every step I go through to tackle new Machine Learning problems.
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Here is every step I go through to tackle new Machine Learning problems.
Step
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https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">What exactly is the problem that you need to solve?
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">Why do you need to solve this problem?
The answers should give you all the information you need to ensure a solid solution.
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The answers should give you all the information you need to ensure a solid solution.
Step
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https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">What data do you have access to?
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">What& #39;s the format of that data?
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">How is that data going to be renewed/expanded?
Then you can focus on cleaning up the data and making it ready to solve the problem.
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Then you can focus on cleaning up the data and making it ready to solve the problem.
Step
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https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">How would you solve this problem?
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">What are some algorithms that you could use?
For each potential solution:
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">Determine success metric.
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">Build a quick experiment.
Keep any promising candidate solutions. Discard the rest.
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For each potential solution:
Keep any promising candidate solutions. Discard the rest.
Step
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Iteratively try to improve each candidate& #39;s solution, until one surfaces as the best approach.
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">Pick the best candidate.
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">Improve its results.
At this point, you don& #39;t need the ultimate best solution. You need a working, decent solution.
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Iteratively try to improve each candidate& #39;s solution, until one surfaces as the best approach.
At this point, you don& #39;t need the ultimate best solution. You need a working, decent solution.
Step
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https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">Does the solution match the problem?
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">Is the solution good enough?
https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">Are the trade-offs and limitations acceptable?
Move back one or two steps if the evaluation is not successful.
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Move back one or two steps if the evaluation is not successful.
Step
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https://abs.twimg.com/emoji/v2/... draggable="false" alt="▫️" title="Weißes kleines Quadrat" aria-label="Emoji: Weißes kleines Quadrat">How is this solution going to be accessed?
This step is about deploying and operationalizing the Machine Learning solution you just built.
It doesn& #39;t matter how good are the predictions of a model if you can& #39;t connect them to real data and real users.
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This step is about deploying and operationalizing the Machine Learning solution you just built.
It doesn& #39;t matter how good are the predictions of a model if you can& #39;t connect them to real data and real users.