Having established what machine learning is and how it differs from traditional programming, we now turn to the essential vocabulary and concepts that form the building blocks of any machine learning project. Understanding these terms is necessary for effectively working with algorithms and data.

This chapter introduces the core components you'll encounter repeatedly:

• Data: We will examine why data is fundamental and discuss the concepts of features (inputs) and labels (outputs).
• Data Splitting: You'll learn why datasets are typically divided into training, validation, and test sets.
• Models: We will define what a machine learning model is and how it represents learned patterns.
• Parameters vs. Hyperparameters: We'll differentiate between the internal settings a model learns (parameters) and the external configurations you set before training (hyperparameters).
• Common Issues: We'll introduce two frequent challenges in model training: overfitting and underfitting.
• Evaluation Basics: You will get acquainted with simple metrics used to measure a model's performance, such as accuracy.

By the end of this chapter, you will have a solid grasp of the terminology needed to understand the machine learning processes discussed in subsequent sections.