Machine Learning Zoomcamp Update: Wednesday, 15 September 2021
Date: 15 September 2021
Today, I completed the Sessions 2.6, 2.7, 2.8, 2.9 and 2.10. I completed it quickly as the topics were simple.
Session 2.6 - Linear regression: vector form
This session formulated the linear regression prediction equation in vector form.
Session 2.7 - Training linear regression: Normal equation
This session derived and explained the normal equation.
Key takeaways:
- Since, feature matrices are not square we cannot accurately calculate the weights to make predictions.
- We multiple the feature matrix with its transpose to make it square.
- Normal equation: W = (X^{T}X)^{-1}X^{T}y
Session 2.8 - Baseline model for car price prediction project
This session created a baseline model.
Session 2.9 - Root mean squared error
This session defined the root mean squared error which is a metric for evaluating regression models.
Key takeaways:
- RMSE = √(^{1}⁄_{m}*∑_{i=1}^{m} (g(x_{i}) - y_{i})^{2})
Session 2.10 - Computing RMSE on validation data
This session computed RMSE on validation data for the baseline model.
Estimated Time Taken: 35 minutes