What you will learn in the Mathematics for Machine Learning: Multivariate Calculus Course
Understand the foundational concepts of multivariate calculus essential for machine learning.
Learn to compute gradients and directional derivatives in multiple dimensions.
Apply calculus to optimize functions using gradient descent.
Explore the role of calculus in training neural networks and linear regression models.
Develop an intuitive understanding of calculus to enhance machine learning proficiency.
Program Overview
What is Calculus?
⏳ 3 hours
- Introduction to the concept of calculus and its relevance to machine learning.
Multivariate Calculus
⏳ 3 hours
- Exploration of functions of multiple variables and partial derivatives.
Gradient Descent
⏳ 3 hours
- Understanding the gradient descent algorithm and its application in optimization.
Neural Networks and Backpropagation
⏳ 3 hours
- Study of how calculus is used in training neural networks through backpropagation.
Linear Regression Models
⏳ 3 hours
- Application of calculus in fitting linear regression models.
Final Project
⏳ 3 hours
- Hands-on project to apply the learned concepts in a practical scenario.
Job Outlook
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Job Outlook
- Enhances mathematical proficiency for careers in data science, machine learning, and artificial intelligence.
Provides a solid foundation for advanced studies in machine learning algorithms and neural networks.
Completing this course can bolster qualifications for roles requiring strong analytical and problem-solving skills.
Specification: Mathematics for Machine Learning: Multivariate Calculus Course
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