Calculus for Machine Learning and Data Science
Completed by John Martin Patterson
March 2, 2026
26 hours (approximately)
John Martin Patterson's account is verified. Coursera certifies their successful completion of Calculus for Machine Learning and Data Science
What you will learn
Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients
Approximately optimize different types of functions commonly used in machine learning
Visually interpret differentiation of different types of functions commonly used in machine learning
Perform gradient descent in neural networks with different activation and cost functions
Skills you will gain
- Category: Applied Machine Learning
- Category: Derivatives
- Category: Machine Learning Methods
- Category: Applied Mathematics
- Category: Python Programming
- Category: Software Visualization
- Category: Advanced Mathematics
- Category: Calculus
- Category: Machine Learning
- Category: Machine Learning Algorithms
- Category: Computer Programming
- Category: Numerical Analysis
