10 Data Preparation
Machine learning algorithms can only work as well as the data they’re trained on. In the real world, our data can come from noisy sensors, computer programs with bugs, or even incomplete or inaccurate transcriptions of paper records. We always need to look at our data and fix any problems before we use it.
A rich body of methods has been developed for just this job. They’re referred to as techniques for data preparation, or data cleaning. The idea is to process our data before learning from it so that our learning systems can use the data most efficiently.
We also want to make sure that the data itself is well suited ...
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