- Python Programming
- Calculus
- Numerical Analysis
- Machine Learning Algorithms
- Artificial Neural Networks
- Derivatives
- Deep Learning
- Machine Learning
- Applied Mathematics
- Visualization (Computer Graphics)
Calculus for Machine Learning and Data Science
Completed by Vaishnavi Jawalkar
September 16, 2024
26 hours (approximately)
Vaishnavi Jawalkar'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
