From the course: Data Science Foundations: Fundamentals
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Passive collection of training data
From the course: Data Science Foundations: Fundamentals
Passive collection of training data
- [Narrator] Some things you can learn faster than others, and food is a good example. A lot of people love eating mussels, but some people eat them get sick and then never want to touch them again. And I'm one of those people. This is called a conditioned taste aversion and results from something that is psychologically unusual, and that's one trial learning. You only have to do it once to get it ingrained into your behavior. But if you're working in data science and especially if you're training a machine learning algorithm, you're going to need a lot more than one trial for learning. You're going to need an enormous amount of labeled training data to get your algorithm up and working properly. One of the interesting things about data science is that gathering enormous amounts of data doesn't always involve enormous amounts of work. In certain respects, you can just sit there and wait for it to come to you. That's the…
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Contents
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Data preparation5m 26s
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(Locked)
Labeling data8m 48s
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(Locked)
In-house data5m 38s
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(Locked)
Open data4m 15s
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(Locked)
APIs2m 40s
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(Locked)
Scraping data4m 44s
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(Locked)
Creating data5m 36s
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(Locked)
Passive collection of training data3m 57s
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(Locked)
Self-generated data3m 30s
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(Locked)
Data vendors5m 30s
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(Locked)
Data ethics5m 14s
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