![]() This is a beginner-friendly program, with a recommended background of at least high school mathematics. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques - plus the know-how to incorporate them into your machine learning career. 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. 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. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. Apply concepts of eigenvalues and eigenvectors to machine learning problems.Express certain types of matrix operations as linear transformations.Apply common vector and matrix algebra operations like dot product, inverse, and determinants.Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.Understand the properties of commonly used probability distributions in machine learning and data scienceĪpply common statistical methods like MLE and MAPĪssess the performance of machine learning models using interval estimates and margin of errorsĪpply concepts of statistical hypothesis testingĪfter completing this course, learners will be able to: Perform gradient descent in neural networks with different activation and cost functionsĭescribe and quantify the uncertainty inherent in predictions made by machine learning models Optimize different types of functions commonly used in machine learning ![]() ![]() Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independenceĪpply common vector and matrix algebra operations like dot product, inverse, and determinantsĮxpress certain types of matrix operations as linear transformationsĪpply concepts of eigenvalues and eigenvectors to machine learning problems By the end of this Specialization, you will be ready to: ![]()
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