Notes

Support vector machines can be used for regression, classification, and outlier detection.

Here I breakdown the difference between unsupervised and supervised learning, and the most known algorithms used in each.

Neural networks are the building blocks of deep learning. Here we break down how they work.

K-means is one of the most common algorithms to perform unsupervised clustering.

Linear regression is useful for finding the linear relationship between the independent and dependent variables of a dataset.

Logistic regression uses a sigmoid function to perform binary classification, i.e. when you can linearly separate two classes of data from each other.