Machine Learning Classes

There are several kinds of machine learning algorithms depending on the objective and provided data. We will discuss some of them below:

Supervised Learning & Unsupervised Learning

The supervised and unsupervised classes of learning are the most popular types of machine learning algorithms. If the provided data has a label on it, then it is supervised learning. If the process or algorithm does not need or have any label, then it is unsupervised learning.

The above model for the vehicle dataset is considered supervised learning if you want to predict the price or any other feature based on the rest of the attributes. The same data can be used for unsupervised learning if you want to cluster similar vehicles or detect outlier vehicles.

Usually, unsupervised learning is a little harder than supervised learning since it is difficult to determine the loss, or goodness, of a model when there is no label to compare to. For clustering a dataset, you can use the distance between the mean of two clusters or the inverse distance between points in similar clusters as a determiner of the "goodness" of the model. For now, we will learn many supervised learning methods. Later on in the series, however, we will go through unsupervised learning methods such as data clustering, outlier detection, data augmentation, synthesis, and many other applications.