It is well known that most users do not read privacy policies but almost always tick the box to agree with them. While the length and readability of privacy policies have been well studied and many approaches for policy analysis based on natural language processing have been proposed, existing studies are limited in their depth and scope, often focusing on a small number of data practices at single point in time. In this article, we fill this gap by analyzing the 25-year history of privacy policies using machine learning and natural language processing and presenting a comprehensive analysis of policy contents. Specifically, we collect a large-scale longitudinal corpus of privacy policies from 1996 to 2021 and analyze their content in terms of the data practices they describe, the rights they grant to users, and the rights they reserve for their organizations. We pay particular attention to changes in response to recent privacy regulations such as the GDPR and CCPA. We observe some positive changes, such as reductions in data collection post-GDPR, but also a range of concerning data practices, such as widespread implicit data collection for which users have no meaningful choices or access rights. Our work is an important step toward making privacy policies machine readable on the user side, which would help users match their privacy preferences against the policies offered by web services.