Machine Learning 101, as Explained by Google

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googleby Angela Guess

Danny Sullivan recently wrote for Marketingland, “What exactly is ‘machine learning’ and how do machines teach themselves? Here’s some background drawn from those involved with machine learning at Google itself. Yesterday, Google held a Machine Learning 101 event for a variety of technology journalists. I was one of those in attendance. Despite the billing as an introduction, what was covered still was fairly technical and hard to digest for me and several others in attendance. For example, when a speaker tells you the math with machine learning is “easy” and mentions calculus in the same sentence, they have a far different definition of easy than the layperson, I’d say! Still, I came away with a much better understanding of the process and parts involved with how machines — computers — learn to teach themselves to recognize objects, text, spoken words and more. Here’s my takeaway.”

Sullivan goes on, “Machine learning systems are made up of three major parts, which are: (1) Model: the system that makes predictions or identifications. (2) Parameters: the signals or factors used by the model to form its decisions. (3) Learner: the system that adjusts the parameters — and in turn the model — by looking at differences in predictions versus actual outcome… Everything starts with the model, a prediction that the machine learning system will use. The model initially has to be given to the system by a human being, at least with this particular example. In our case, the teacher will tell the machine learning model to assume that studying for five hours will lead to a perfect test score.”

Read more here.

photo credit: Google

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