Participants in Stanford’s Machine Learning Specialization implement a univariate linear regression model from scratch using Python. The approach fits a line to coffee-consumption and productivity score data, employing gradient descent to minimize mean squared error. This exercise illustrates foundational ML principles without relying on prebuilt libraries.
Key points
- Manual implementation of univariate linear regression using Python on coffee consumption vs productivity data
- Use of mean squared error cost function with 1/(2m) factor and gradient descent for parameter optimization
- Visualization of iterative fitting via scatter plot and regression line over 1000 epochs with learning rate 0.01
Why it matters: Hand-coding ML algorithms deepens understanding of optimization and promotes transparent, trustworthy modeling across AI applications.
Q&A
- How do I choose the right learning rate?
- How can I tell if gradient descent has converged?
- Why implement linear regression manually instead of using libraries?
- How would you extend this approach to multiple variables?
