8NOVEMBER 2020IN MYOPINIONBEST PRACTICES FOR SELECTING AND IMPLEMENTING MACHINE LEARNING SYSTEMSBy Srinivas Krovvidy, Director & Head of Advanced Analytics Enablement, Fannie MaeWalk into any executive-level meeting these days, and you'll hear plenty of chatter about machine learning (ML) and artificial intelligence (AI), but what do they really mean? ML is complex because it includes concepts from multiple areas, such as mathematics, computer science, statistics, and logic. These are all highly technical and seldom explained in a simple manner. Let us introduce a few key ML and AI technical terms and how people apply these techniques and share some best practices and challenges for implementing ML systems using these technologies.What are some basic terms you need to know?ML is artificially intelligent self-learning system that use data mining, pattern recognition, and natural language processing to mimic human reasoning. It includes a training phase where the system learns from training data. For instance, a ML algorithm can learn to predict health or disease by analyzing all of the data generated by medical specialists. AI is the study of how to make computers do things that people are better at, or would be better at, if they could extend what they do to a large amount of data and not make mistakes.Supervised learning systems leverage training data of input values and associated output values also called labeled data. For example, a ML system can be trained using a set of images of cats and dogs, and when given a new image, the system can then predict whether it is a cat or a dog. Similarly, a supervised learning system can leverage logistic regression techniques and historical data to predict the value of a variable such as future market indices.Unsupervised learning systems have an ability to learn and figure things out from unlabeled data. For example, an unsupervised ML system can learn how to group (also called clustering) a series of news articles under different categories without explicitly being told how to do it.Reinforcement learning systems have the ability to not only learn from training data, but also improve their performance by processing external feedback. For example, the system that selects your favorite music list uses your feedback from previous choices and improves its selections on an ongoing basis.
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