
Explore supervised learning by distinguishing inputs and labels, training with features to predict outputs, and applying regression and classification tasks such as house prices and spam detection.
Explore how the gradient descent learning rate controls step size to reach a minimum: too large causes overshoot and oscillation, too small slows convergence, so choose an appropriate rate.
This course is designed to understand basic Concept of Machine Learning. Anyone can opt for this course. No prior understanding of Machine Learning is required. Simple Linear Regression Concepts are covered in detail. Coding part is not covered, however wherever possible I have attached the code in the resources.
Now question is why this course?
This Course will not only teach you the basics of Machine learning and Simple Linear Regression. It will also cover in depth mathematical explanation of Cost function and use of Gradient Descent for Simple Linear Regression. Understanding these is must for a solid foundation before entering into Machine Learning World. This foundation will help you to understand all other algorithms and mathematics behind it.