Feature Engineering for Numerical and Image Data

In most cases, when we design large-scale machine learning systems, the types of data we get require more processing than just visualization. This visualization is only for the design and development of machine learning systems. During deployment, we can monitor the data, as we discussed in the previous chapters, but we need to make sure that we use optimized data for inference.

Therefore, in this chapter, we’ll focus on feature engineering – finding the right features that describe our data closer to the problem domain rather than closer to the data itself. Feature engineering is a process where we extract and transform variables from raw data so that we can use them for predictions, classifications, ...

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