Highlights: Welcome back to the all-new series on Machine Learning. In the previous post, we gave you a sneak peak into the basics of Machine Learning, the two types of Machine Learning, viz., Supervised & Unsupervised, and implemented some examples using various algorithms in each of the techniques. In this new tutorial post, we will explore one of the most widely used Supervised Learning algorithms in the world today – Linear Regression. We will start off with some theory and go on to build a simple model in Python, from scratch. In our previous post (also the first post of this Machine Learning tutorial series), we brushed the fundamentals of Linear Regression using the example of housing price prediction, given the size of the house. If you remember, the prediction was based on the linear relationship that existed between the house price and the size of the house. Have a look at the image below. In the graph above, the size of the house is shown along the horizontal axis and the price of a house is shown along the vertical axis. Here, each data point is a house with its respective size and the price that the house was recently sold for.
Aug-10-2022, 07:55:49 GMT