While a strong background in mathematics can be beneficial when delving deeper into machine learning, it is not an absolute requirement to get started. The level of mathematical knowledge you need depends on your goals within the field of machine learning.

Here's a breakdown:

Basic Understanding: For beginners, a basic understanding of mathematics, including algebra and statistics, is sufficient to start learning and using machine learning frameworks and libraries. You can grasp the concepts and start building simple machine learning models with this foundation.

Intermediate Knowledge: As you progress, having some familiarity with concepts like calculus and linear algebra becomes more valuable, especially when you want to understand the underlying algorithms and concepts in more detail. Linear algebra, for example, is crucial when dealing with matrices and tensors, which are common in machine learning.

Advanced Mathematics: For advanced machine learning research or specialized roles, a deeper understanding of advanced mathematical topics such as optimization theory, probability theory, and differential equations can be beneficial. These concepts are often used when developing new algorithms or conducting research in machine learning.

The key is to start at a level that matches your current skills and gradually build your mathematical knowledge as you progress in your machine learning journey. Many machine learning frameworks and libraries offer high-level APIs that allow you to work with machine learning without diving deeply into the mathematical details.

Additionally, there are various online courses, tutorials, and resources that cater to individuals with different levels of mathematical background, making it accessible to a broader audience. So, while mathematics can enhance your understanding and capabilities in machine learning, it's not a barrier to entry for beginners.