Course Description
Machine Learning is everywhere these days, often invisible to most of us. Discover one of the fundamental problems in the world of ML - linear regression. Explore how this is solved using classic ML as well as Neural Networks.
Learning Objectives
Linear Regression Models: Introduction to Linear Regression
- Course Overview
- define what regression is and recall how it can be used to represent a relationship between two variables
- identify the applications of regression and recognize why it is used to make predictions
- describe how to evaluate the quality of a regression model by measuring its loss
- recognize the specific relationship which needs to exist between the input and output of a regression model
- describe the technique used in order to make predictions with regression models
- compare classic ML and deep learning techniques to perform a regression
- identify the various components of a neural network such as neurons and layers and how they fit together
- recall the two types of functions used in a neuron and their individual roles
- describe the configurations required to use a neuron for linear regression
- list the steps involved in calculating the optimal weights and biases of a neural network
- define the technique of gradient descent optimization in order to find the optimal parameters for a neural network
- recall key concepts of linear regression and deep learning