Abstract

Machine learning is a subset of artificial intelligence (AI) that provides machines with the ability to automatically learn from data without being explicitly programmed. It is a combined field of computer science, mathematics and statistics to create a predictive model by learning patterns in a dataset. The dataset may have an output field which makes the learning process supervised. The supervised learning methods in machine learning have outputs (also called as targets or classes or categories) defined in the datasets in a column. These targets can either be integers or real (continuous) numbers. When the targets are integers, the learning task is known as classification. Each row in the dataset is a sample and the classification is assigning a class label/target to each sample. The algorithm which is used for this learning task is called a classifier. When the targets are real numbers, the learning task is called regression and the algorithm which is used for this task is called a regressor. We will go through classification first and look at regression later in this tutorial.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• what are classification and regression techniques?
• How they can be used for prediction?
• How visualizations can be used to analyze predictions?

Learning Objectives

• Explain the types of supervised machine learning - classification and regression.
• Learn how to make predictions using the training and test dataset.
• Visualize the predictions.