We are, in all likelihood, living in the most defining period of human history. The period when computing moved from large mainframes to PCs and then to the cloud. But what makes it more defining is what is coming in our way in the upcoming years. What makes this period exciting and captivating for someone is the normalization of the various tools and techniques, which followed the boost in computing. Widely, Machine Learning Algorithms are classified as: 1. Supervised Learning : Consists of a target / output variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function which map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Some of the examples of Supervised Learning are Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc. 2. Unsupervised Learni...
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