After completing this course, learners will be able to: â€¢ Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc. â€¢ Apply common vector and matrix algebra operations like dot product, inverse, and determinants â€¢ Express certain types of matrix operations as linear transformations â€¢ Apply concepts of eigenvalues and eigenvectors to machine learning problems Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where youâ€™ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills.Â This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, youâ€™ll understand the mathematics behind all the most common algorithms and data analysis techniques â€” plus the know-how to incorporate them into your machine learning career. This is a beginner-friendly program, with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where theyâ€™re most applicable to machine learning and data science. If you are already familiar with the concepts of linear algebra, Course 1 will provide a good review, or you can choose to take Course 2 of this specialization, Calculus for Machine Learning and Data Science now, and Course 3, Probability and Statistics for Machine Learning and Data Science when it is released in April.