Supervised Learning

Before diving into the many algorithms of machine learning, it is important to take a step back and understand the big picture associated with the entire field.

Linear regression is one of the most fundamental algorithms in machine learning. In this lesson, learn how linear regression works!

The perceptron algorithm is an algorithm for classifying data. It is the building block of neural networks.

Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.

Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data. Specifically Naive Bayes is frequently used with text data and classification problems.

Support vector machines are a common method used for classification problems. They have been proven effective using what is known as the 'kernel' trick!

Bagging and boosting are two common ensemble methods for combining simple algorithms to make more advanced models that work better than the simple algorithms would on their own.

Learn the main metrics to evaluate models, such as accuracy, precision, recall, and more!

Learn the main types of errors that can occur during training, and several methods to deal with them and optimize your machine learning models.

You've covered a wide variety of methods for performing supervised learning -- now it's time to put those into action!