We've been learning about regression, and even coded our own very simple linear regression algorithm. Along with that, we've also built a coefficient of determination algorithm to check for the accuracy and reliability of our best-fit line. We've discussed and shown how a best-fit line may not be a great fit, but also explained why our example was correct directionally, even if it was not exact. Now, however, we are at the point where we're using two top-level algorithms, which are subsequently comprised of a handful of smaller algorithms. As we continue building this hierarchy of algorithms, we might wind up finding ourselves in trouble if just one of them have a tiny error, so we want to test our assumptions.
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.