There is a common confusion between libraries and frameworks. So before we move on to introducing the top 15 machine learning libraries and their benefits, let’s explore the key distinction between libraries and frameworks.Libraries provide specific functionalities, while frameworks offer a complete set of tools for developing a fully-fledged application. So when designing a software solution, you might use many libraries, but typically only one or a few frameworks.A library is a collection of prewritten codes, predefined methods, and classes that programmers can use to simplify and accelerate development and solve a specific problem. It includes functions, class definitions, important constants, etc. As a result, you can skip writing code to achieve specific features.Most programming languages include a standard library, but developers can create their own customized ones. Python has a large set of special-purpose libraries for scraping information, visualizing data, designing ML models, etc.A framework is a package of code libraries, compilers, APIs, and other supporting programs that provides standard functionality for programmers to speed up the software development process. Frameworks give you a structure for building an app and often include pre-built code that can be used to accomplish common tasks or modified to better fit the needs of a specific project.In this article, we give an overview of the most popular ML libraries written in Python and other programming languages. If you are not yet familiar with the process of using external libraries in Python, we recommend reading this step-by-step guide.Now that we have clarified the definitions, it’s time to get to the list of top open-source ML libraries that we have compiled in collaboration with our AI experts.