This chapter establishes the groundwork for understanding how probability and statistics are applied in machine learning. We begin by defining these fields and illustrating their direct relevance to building and interpreting machine learning models.

You will learn to:

• Define probability and statistics in the context of data analysis.
• Recognize why these concepts are fundamental to machine learning tasks.
• Distinguish between different types of data (e.g., categorical, numerical).
• Understand the difference between a population and a sample.
• Set up a basic Python environment using libraries like NumPy and Pandas.
• Load and perform initial checks on a dataset using Python.

By the end of this chapter, you'll have a clear understanding of the foundational concepts and be ready to perform initial data loading and inspection, preparing you for the statistical techniques covered later.