In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM.Read more.
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The techniques and tools covered in Introduction to Machine Learning: Supervised Learning are most similar to the requirements found in Data Scientist job advertisements.
