Discussing the principles behind the most used Machine Learning algorithms What you'll learn

Machine Learning Principles

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Regression (Linear Regression, Multiple Linear Regression, Polynomial Regression, and Support Vector Regression)

Classification (Logistic Regression, k-Nearest Neighbours, Trees, and Support Vector Machines)

Other principles such as Cross Validation, AIC, BIC, and choosing the right metrics for your algorithm

Requirements

An interest in knowing machine learning from first principles without jumping straight into coding

Description

This course is intended to introduce the principles behind the algorithms and concepts in Machine Learning. Understanding these will help you to take your Machine Learning skills to the next level. As Machine Learning is a tool, without understanding the principles, one will not fully utilize it and come up with valuable insights. What does it mean to have an MSE of 50 000? Why does this ML model work better than the other one? What is the best metric for the problem at hand - Accuracy, Specificity or Recall?

Who this course is for:

Bners who are curious to start their understanding of Machine Learning without jumping head-first into the codes