fix: MSE→RMSE terminology consistency and classification report table alignment

2-Regression/3-Linear/README.md

@@ -207,13 +207,13 @@ lin_reg.fit(X_train,y_train)
 
 The `LinearRegression` object after `fit`-ting contains all the coefficients of the regression, which can be accessed using `.coef_` property. In our case, there is just one coefficient, which should be around `-0.017`. It means that prices seem to drop a bit with time, but not too much, around 2 cents per day. We can also access the intersection point of the regression with Y-axis using `lin_reg.intercept_` - it will be around `21` in our case, indicating the price at the beginning of the year.
 
-To see how accurate our model is, we can predict prices on a test dataset, and then measure how close our predictions are to the expected values. This can be done using mean square error (MSE) metrics, which is the mean of all squared differences between expected and predicted value.
+To see how accurate our model is, we can predict prices on a test dataset, and then measure how close our predictions are to the expected values. This can be done using root mean square error (RMSE) metrics, which is the root of the mean of all squared differences between expected and predicted value.
 
 ```python
 pred = lin_reg.predict(X_test)
 
-mse = np.sqrt(mean_squared_error(y_test,pred))
-print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')
+rmse = np.sqrt(mean_squared_error(y_test,pred))
+print(f'RMSE: {rmse:3.3} ({rmse/np.mean(pred)*100:3.3}%)')
 ```
 
 Our error seems to be around 2 points, which is ~17%. Not too good. Another indicator of model quality is the **coefficient of determination**, which can be obtained like this:

4-Classification/2-Classifiers-1/README.md

@@ -223,7 +223,7 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
     | japanese     | 0.70      | 0.75   | 0.72     | 220     |
     | korean       | 0.86      | 0.76   | 0.81     | 242     |
     | thai         | 0.79      | 0.85   | 0.82     | 254     |
-    | accuracy     | 0.80      | 1199   |          |         |
+    | accuracy     |           |        | 0.80     | 1199    |
     | macro avg    | 0.80      | 0.80   | 0.80     | 1199    |
     | weighted avg | 0.80      | 0.80   | 0.80     | 1199    |