1. Feature Scaling

• feature scaling is about rescaling the training set values to make gradient descent easier.

2. Ways to do Feature Scaling

1. Deviding the values by maximum possible value.
2. Mean Normalization:
   
3. Z-score normalization: how many standard deviation away is the value from the mean?
   

Aim for -1 <= x <= 1.

3. Choosing the Learning Rate

• Learning Graph is a graph of J(w,b) value by iterations.
• As iterations get larger, the cost value should decrease and converge.
• Learning rate should not be too large or small. If too large, cost may increase instead of decrease.

• Choose a small value and try multiplying it by 3x to look for the steepest learning curve that constantly decreases.

4. Feature Engineering

• Feature engineering involves combining or transforming the original features in the problem to get a much better model.

5. Polynomial Regression

• Feature scaling is important for cubic model, because the number range would get very different for each features.
  
• Square rooting the size and adding it as a new feature can make the model more fitting.

6. Practice Lab

0.08𝑥+0.54𝑥^2+0.03𝑥^3+0.0106

• for y = x**2, the gradient descent shows a greater weight for x^2 alpha value, because it is the feature closest with the y value.
• Using feature engineering, even non-linear functions can be plotted with linear regression.
• Feature scaling is important for feature engineering, especially with larger exponents.