Calculus for Machine Learning and Data Science
Master calculus fundamentals for ML and data science, from derivatives to optimization. Perfect for intermediate learners seeking practical math skills.
Master calculus fundamentals for ML and data science, from derivatives to optimization. Perfect for intermediate learners seeking practical math skills.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Mathematics for Machine Learning and Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.8
(708 ratings)
55,253 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Analytically optimize machine learning functions using derivatives and gradients
Implement gradient descent in neural networks with various activation functions
Visualize and interpret differentiation of ML functions
Apply optimization techniques to real-world machine learning problems
Master Newton's method for advanced optimization
Skills you'll gain
This course includes:
4.3 Hours PreRecorded video
5 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This comprehensive course covers essential calculus concepts for machine learning and data science. The curriculum focuses on derivatives, optimization techniques, gradient descent, and their applications in neural networks. Through hands-on Python programming exercises and visual explanations, students learn to apply mathematical concepts to real-world machine learning problems. The course emphasizes practical implementation alongside theoretical understanding, making complex mathematical concepts accessible and applicable.
Derivatives and Optimization
Module 1 · 8 Hours to complete
Gradients and Gradient Descent
Module 2 · 7 Hours to complete
Optimization in Neural Networks and Newton's Method
Module 3 · 10 Hours to complete
Fee Structure
Instructor
Quantum AI Research Scientist and Educator
Luis Serrano is an accomplished AI scientist, popular YouTuber, and author of the book "Grokking Machine Learning." Currently, he serves as a quantum AI research scientist at Zapata Computing in Toronto, where he develops machine learning algorithms for quantum computers. Previously, he held significant roles in Silicon Valley, including lead AI educator at Apple, head of content for AI and Data Science at Udacity, and a member of the video recommendations team at Google’s YouTube. With a strong academic background, including a Bachelor's and Master's from the University of Waterloo and a PhD from the University of Michigan, Luis has a deep passion for mathematics that began in his youth when he represented Colombia in the International Mathematical Olympiads. Through his work and online presence, he aims to make complex AI concepts accessible to a broader audience while continuing to contribute to the advancement of quantum computing.
Testimonials
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4.8 course rating
708 ratings
Frequently asked questions
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