A course on mathematical methodology for data science and machine learning applications. Discover the methodologies needed to understand the mathematical foundations of the field.
Suggested by: Coursera (What is Coursera?)
No prior knowledge required
No unnecessary risks
In many high-level courses in machine learning and data science, you’ll find that you need to brush up on basic math—things you learned in school or college, but were presented in a different context, or weren’t very clear, so it’s hard for you to relate them to how they’re used in computer science. This specialization is designed to bridge that gap, connecting you to the basic math, building an intuitive understanding, and connecting that to machine learning and data science.
We’ll look at what linear algebra is and how it relates to data. Then we’ll look at what vectors and matrices are and how to work with them.
It builds on this to examine how to optimize fitting functions to achieve a good fit to the data. It starts with introductory calculus and then uses matrices and vectors from the first course to examine the fit to the data.
Uses math from previous courses to compress high-dimensional data. This course is intermediate level and requires knowledge of Python and numpy.
At the end of this internship, you will gain the mathematical knowledge required to continue your journey and take more advanced courses in machine learning.
Through the assignments of this internship, you will use the skills you have learned to create small projects in Python on interactive notebooks, an easy-to-learn tool that will help you apply your knowledge to real-world problems. for example:
