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In this chapter, we begin the machine learning workflow with one of the most critical steps: loading data. Before any model can be trained or evaluated, raw data must be brought into the system in a usable form.

This video introduces how machine learning practitioners work with data from real-world sources such as log files, dataset files, and databases. Since data often comes from multiple sources, we explore practical methods for importing and managing data efficiently.

You’ll learn how to load data from common formats like CSV files and SQL databases, along with techniques for generating simulated datasets that have desirable properties for experimentation and testing algorithms.

While Python offers many ways to handle data ingestion, this chapter focuses on:

Using pandas for loading and handling external data

Using scikit-learn to generate synthetic datasets for machine learning experiments

By the end of this chapter, you’ll have a strong foundation in bringing data into your machine learning pipeline—setting the stage for cleaning, preprocessing, and modeling in later chapters.