Machine Learning for OpenCV 4 - Second Edition
by Aditya Sharma, Michael Beyeler (USD), Vishwesh Ravi Shrimali, Michael Beyeler
Summary
In this chapter, we talked about machine learning at a high abstraction level: what it is, why it is important, and what kinds of problems it can solve. We learned that machine learning problems come in three flavors: supervised learning, unsupervised learning, and reinforcement learning. We talked about the prominence of supervised learning, and that this field can be further divided into two subfields: classification and regression. Classification models allow us to categorize objects into known classes (such as animals into cats and dogs), whereas regression analysis can be used to predict continuous outcomes of target variables (such as the sales price of used cars).
We also learned how to set up a data science environment using ...
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