Let's start with the basics and gradually move towards more complex concepts. Machine Learning (ML) is a subset of artificial intelligence. At its core, ML is the study of computer algorithms that can improve automatically through experience and the use of data. Essentially, it is the process by which computers can learn and make decisions or predictions without being explicitly programmed to perform the task. Machine Learning Basics To illustrate the basics of machine learning, consider a simple example. Let's say you're trying to distinguish between emails that are spam and those that are not. How would you do it?  You might start by noting certain characteristics or features of spam emails, such as the presence of words like "lottery", "prize", "winner", etc., or emails that have only images. Then you could create a rule-based system that classifies an email as spam if it has these features. However, spammers are clever and constantly change their tactics. So, maintaining such a rule-based system could become an unending task.  This is where machine learning comes in. Instead of manually creating and updating rules, you could use a machine learning algorithm. You would provide this algorithm with a set of emails which have already been labeled as either spam or not spam (this is your training data). The algorithm would then find patterns and features in the data that correlate with spam. Once trained, it can use this knowledge to classify new emails. This is the basic premise of machine learning, and it can be applied to many different types of problems beyond email classification.  Now, do you have any questions about these basics before we move onto more complex topics such as types of machine learning?