Data Acquisition

Data acquisition is the starting point of any machine learning project, where we gather the raw information needed to teach a model how to make accurate predictions. Think of it as collecting all the pieces for a big puzzle — if you don’t have all the right pieces, or if some are missing, the final picture won’t be clear.

In machine learning, data can come from just about anywhere: databases, web scraping, sensors, public datasets, or even manually entered records. The goal is to get data that’s not only relevant to the problem but also complete, diverse, and as free of errors as possible. After all, the quality of data directly impacts the quality of the model we can build from it.

This chapter will explore different ways to gather data and handle the tricky parts, like cleaning up incomplete records, choosing data sources, and making sure the data respects privacy and security guidelines. By the end, you’ll understand the best practices for setting up a strong, reliable data foundation—an essential step for building powerful, trustworthy machine learning models.