chore(i18n,learn): processed translations (#53415)

Co-authored-by: Naomi Carrigan <nhcarrigan@gmail.com>
Co-authored-by: Oliver Eyton-Williams <ojeytonwilliams@gmail.com>

curriculum/challenges/korean/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md

@@ -62,7 +62,7 @@ The `plotImages` function will be used a few times to plot images. It takes an a
 
 ## Cell 5
 
-Recreate the `train_image_generator` using `ImageDataGenerator`. 
+Recreate the `train_image_generator` using `ImageDataGenerator`.
 
 Since there are a small number of training examples, there is a risk of overfitting. One way to fix this problem is by creating more training data from existing training examples by using random transformations.
 
@@ -90,7 +90,7 @@ Run this cell to visualize the accuracy and loss of the model.
 
 Now it is time to use your model to predict whether a brand new image is a cat or a dog.
 
-In this cell, get the probability that each test image (from `test_data_gen`) is a dog or a cat. `probabilities` should be a list of integers. 
+In this cell, get the probability that each test image (from `test_data_gen`) is a dog or a cat. `probabilities` should be a list of integers.
 
 Call the `plotImages` function and pass in the test images and the probabilities corresponding to each test image.
 

curriculum/challenges/korean/11-machine-learning-with-python/machine-learning-with-python-projects/rock-paper-scissors.md

@@ -12,9 +12,9 @@ For this challenge, you will create a program to play Rock, Paper, Scissors. A p
 
 You will be <a href="https://replit.com/github/freeCodeCamp/boilerplate-rock-paper-scissors" target="_blank" rel="noopener noreferrer nofollow">working on this project with our Replit starter code</a>.
 
--   Start by importing the project on Replit. 
--   Next, you will see a `.replit` window. 
--   Select `Use run command` and click the `Done` button. 
+-   Start by importing the project on Replit.
+-   Next, you will see a `.replit` window.
+-   Select `Use run command` and click the `Done` button.
 
 We are still developing the interactive instructional part of the machine learning curriculum. For now, you will have to use other resources to learn how to pass this challenge.
 
@@ -30,7 +30,7 @@ The file `RPS.py` shows an example function that you will need to update. The ex
 
 ## Development
 
-Do not modify `RPS_game.py`. Write all your code in `RPS.py`. For development, you can use `main.py` to test your code. 
+Do not modify `RPS_game.py`. Write all your code in `RPS.py`. For development, you can use `main.py` to test your code.
 
 `main.py` imports the game function and bots from `RPS_game.py`.
 
@@ -52,7 +52,7 @@ play(player, quincy, 1000, verbose=True)
 
 Click the "run" button and `main.py` will run.
 
-## Testing 
+## Testing
 
 The unit tests for this project are in `test_module.py`. We imported the tests from `test_module.py` to `main.py` for your convenience. If you uncomment the last line in `main.py`, the tests will run automatically whenever you hit the "run" button.