For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.
The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.
The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.
At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.
Coursera
Dive into the core of Mathematics for Machine Learning with our Linear Algebra course. Learn about vectors, matrices, eigenvalues, eigenvectors, and apply these concepts to exciting tasks like image rotation and understanding algorithms like PageRank.
Coursera
Discover the fundamental principles of multivariate calculus and their application in machine learning with this beginner-friendly course. Gain a solid understanding of gradients, optimization techniques, and more, all crucial for developing sophisticated ML algorithms.
Coursera
Dive into the essential mathematics behind one of the most popular dimensionality reduction techniques in machine learning: Principal Component Analysis (PCA). This comprehensive course is designed for those with an intermediate understanding of statistics and linear algebra, aiming to equip you with the skills needed to apply PCA effectively in your data science projects. By the end of this course, you'll have a solid grasp on how to minimize reconstruction error and extract meaningful patterns from high-dimensional datasets.