Welcome to the seventh part of our machine learning regression tutorial within our Machine Learning with Python tutorial series. Up to this point, you have been shown the value of linear regression and how to apply it with Scikit Learn and Python, now we're going to dive into how it is calculated. While I do not believe it is necessary to dig into all of the math that goes into every machine learning algorithm (have you dug into the source code of your other favorite modules to see how they do every little thing?), linear algebra is essential to machine learning, and it is useful to understand the true building blocks that machine learning is built upon.
The objective of linear algebra is to calculate relationships of points in vector space. This is used for a variety of things, but one day, someone got the wild idea to do this with features of a dataset. We can too! Remember before when we defined the type of data that linear regression was going to work on was called "continuous" data? This is not so much due to what people just so happen to use linear regression for, it is due to the math that makes it up. Simple linear regression is used to find the best fit line of a dataset. If the data isn't continuous, there really isn't going to be a best fit line
The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.
In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.