- Deep Learning
- Applied Mathematics
- Derivatives
- Artificial Neural Networks
- Numerical Analysis
- Regression Analysis
- Mathematical Modeling
- Machine Learning
- Calculus
- Python Programming
Calculus for Machine Learning and Data Science
Completed by Stirling Kirk Wight
June 25, 2024
26 hours (approximately)
Stirling Kirk Wight'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
