Imperial College London's "Mathematics for Machine Learning" specialization offers comprehensive training in the fundamental mathematical concepts essential for understanding and applying machine learning and data science techniques. The courses are designed to equip learners with the necessary mathematical foundation to delve into advanced machine learning modules and pursue a career in this rapidly evolving field.
The specialization consists of three courses, each focusing on a key area of mathematical theory and its application in machine learning:
Throughout the specialization, learners will engage with interactive coding exercises using Python and Jupyter notebooks, ensuring a practical understanding of the mathematical concepts in a data-driven context. By the end of the program, participants will possess the prerequisite mathematical knowledge to pursue advanced machine learning and data science courses.
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The Mathematics for Machine Learning specialization comprises three courses: Linear Algebra, Multivariate Calculus, and PCA. Each course provides a deep understanding of fundamental mathematical concepts and their application in machine learning and data science.
In the "Linear Algebra" course, you will delve into the fundamental concepts of linear algebra, including vectors, matrices, eigenvalues, and eigenvectors. By implementing these concepts in code, you will gain a practical understanding of their application in machine learning problems.
The "Multivariate Calculus" course offers a comprehensive overview of the multivariate calculus required for building common machine learning techniques. You will explore how calculus plays a crucial role in neural networks and linear regression models, ensuring a solid foundation for advanced machine learning studies.
Through the "Dimensionality Reduction with Principal Component Analysis (PCA)" course, you will implement mathematical concepts using real-world data and master PCA from a projection perspective. This intermediate-level course will equip you with the skills to compress high-dimensional data effectively.
Data for Machine Learning equips learners with essential skills to understand, process, and utilize data in machine learning models effectively.
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