Fundamentals of Supervised Machine Learning

This course aims to provide a comprehensive understanding of supervised machine learning along with the mathematical concepts behind different processes. The course will delve into the mathematical principles underpinning various supervised learning algorithms, ensuring that students gain both theoretical knowledge and practical skills. Students will learn how to design, implement, and evaluate supervised learning models to solve real-world using Python.

• Comprehend Basic Concepts: Explain the fundamental concepts of supervised learning and its mathematical foundations.
• Apply Mathematical Techniques: Use linear algebra, calculus, probability, and statistics to understand and develop machine learning algorithms.
• Develop Models: Understand and implement various supervised learning models including linear regression, logistic regression, support vector machine, decision trees, and neural networks.
• Evaluate Models: Assess model performance analytically using appropriate metrics.
• Employing Information Practically: Solve real-world problems using supervised learning algorithms and infer the results within a given perspective.
• Communicate Effectively: Articulate and communicate mathematical notions and machine learning results in effect to both technical and non-technical audiences.