Machine learning (ML) is everywhere these days, often invisible to most of us. In this 12-video course, you will discover one of the fundamental problems in the world of ML: linear regression. Explore how this is solved with classic ML as well as neural networks. Key concepts covered here include how regression can be used to represent a relationship between two variables; applications of regression, and why it is used to make predictions; and how to evaluate the quality of a regression model by measuring its loss. Next, learn techniques used to make predictions with regression models; compare classic ML and deep learning techniques to perform a regression; and observe various components of a neural network, such as neurons and layers and how they fit together. You will learn the two types of functions used in a neuron and their individual roles; steps involved in calculating the optimal weights and biases of a neural network; and the technique of gradient descent optimization, needed to find optimal parameters for a neural network.