In the last part we introduced Classification, which is a supervised form of machine learning, and explained the K Nearest Neighbors algorithm intuition. In this tutorial, we're actually going to apply a simple example of the algorithm using Scikit-Learn, and then in the subsquent tutorials we'll build our own algorithm to learn more about how it works under the hood.
To exemplify classification, we're going to use a Breast Cancer Dataset, which is a dataset donated to the University of California, Irvine (UCI) collection from the University of Wisconsin-Madison. UCI has a large Machine Learning Repository.
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.