We talk a lot about the concept of connective tissue here at Emerj, the fact that a company that wants to apply AI not only needs to have access to data, not only needs to hire normally very expensive artificial intelligence talent, but also has to have the connective tissue of related subject-matter experts who can work with that talent.
Feature engineering is critical when it comes to the enterprise adoption of AI and for bringing AI projects to life. In laymen's terms, feature engineering is picking the sources and types of data that we're going to use to train the machine. This sounds very simple, but in fact, it's quite challenging, and in this article, we're going to be going through some exact examples of what this looks like and talking about applying it to the real world.
Andrew Ng is one of the towering figures in the world of machine learning. He famously taught at Stanford for quite some time and was with Baidu at one point. Now he runs his own company. He has a quote that ...
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