Math for Machine Learning with R
Build core mathematical foundations for AI development through statistical analysis and computational methods using R programming.
Build core mathematical foundations for AI development through statistical analysis and computational methods using R programming.
Dive into the mathematical foundations crucial for machine learning and artificial intelligence with this comprehensive course. Designed for those looking to strengthen their math skills, it covers key areas such as calculus, linear algebra, probability, and statistics. Through expert instruction and hands-on exercises in R, you'll demystify complex mathematical concepts and learn to apply them in practical machine learning scenarios. Ideal for students and professionals aiming to bridge the gap between basic math knowledge and advanced ML applications.
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What you'll learn
Review and master algebra fundamentals, including quadratic equations and functions
Explore differential calculus foundations, focusing on differentiation and derivatives
Understand vector and matrix operations for analyzing relationships in data
Gain proficiency in statistics fundamentals and probability theory
Apply mathematical concepts using R programming in machine learning contexts
Develop a strong foundation for advanced machine learning and AI studies
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
Module Description
This course bridges the gap between basic mathematical knowledge and the advanced concepts required for machine learning and AI. Students will revisit fundamental algebra, delve into differential calculus, explore vector and matrix operations, and gain insights into statistics and probability. The curriculum is designed to provide a solid mathematical foundation for those pursuing careers in data science, machine learning, and AI. Throughout the course, concepts are reinforced through practical applications using R programming, ensuring students can translate theoretical knowledge into practical skills. By the end of the course, learners will have a robust understanding of the mathematical principles that underpin modern machine learning algorithms and be well-prepared to tackle more advanced topics in the field.
Fee Structure
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