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chore(i18n,learn): processed translations (#50524)

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curriculum/challenges/german/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md
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@@ -47,7 +47,7 @@ sollte zurückgeben:
Notice that the data returned from `get_recommends()` is a list. The first element in the list is the book title passed into the function. The second element in the list is a list of five more lists. Each of the five lists contains a recommended book and the distance from the recommended book to the book passed into the function.
If you graph the dataset (optional), you will notice that most books are not rated frequently. To ensure statistical significance, remove from the dataset users with less than 200 ratings and books with less than 100 ratings.
Wenn du den Datensatz grafisch darstellst (optional), wirst du feststellen, dass die meisten Bücher nicht häufig bewertet werden. To ensure statistical significance, remove from the dataset users with less than 200 ratings and books with less than 100 ratings.
The first three cells import libraries you may need and the data to use. The final cell is for testing. Write all your code in between those cells.

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curriculum/challenges/german/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md
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@@ -20,7 +20,7 @@ For this challenge, you will complete the code to classify images of dogs and c
Some of the code is given to you but some code you must fill in to complete this challenge. Read the instruction in each text cell so you will know what you have to do in each code cell.
The first code cell imports the required libraries. The second code cell downloads the data and sets key variables. The third cell is the first place you will write your own code.
The first code cell imports the required libraries. The second code cell downloads the data and sets key variables. Die dritte Zelle ist der erste Ort, an dem du deinen eigenen Code schreiben wirst.
The structure of the dataset files that are downloaded looks like this (You will notice that the test directory has no subdirectories and the images are not labeled):

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curriculum/challenges/german/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md
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@@ -8,7 +8,7 @@ dashedName: linear-regression-health-costs-calculator
# --description--
You will be <a href="https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb" target="_blank" rel="noopener noreferrer nofollow">working on this project with Google Colaboratory</a>.
Du wirst <a href="https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb" target="_blank" rel="noopener noreferrer nofollow">an diesem Projekt mit Google Colaboratory</a> arbeiten.
After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."

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curriculum/challenges/german/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md
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@@ -8,7 +8,7 @@ dashedName: neural-network-sms-text-classifier
# --description--
You will be <a href="https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb" target="_blank" rel="noopener noreferrer nofollow">working on this project with Google Colaboratory</a>.
Du wirst <a href="https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb" target="_blank" rel="noopener noreferrer nofollow">an diesem Projekt mit Google Colaboratory</a> arbeiten.
After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."

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curriculum/challenges/german/11-machine-learning-with-python/machine-learning-with-python-projects/rock-paper-scissors.md
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@@ -8,7 +8,7 @@ dashedName: rock-paper-scissors
# --description--
For this challenge, you will create a program to play Rock, Paper, Scissors. A program that picks at random will usually win 50% of the time. To pass this challenge your program must play matches against four different bots, winning at least 60% of the games in each match.
Für diese Aufgabe wirst du ein Programm zum Spielen von Schere, Stein, Papier erstellen. A program that picks at random will usually win 50% of the time. To pass this challenge your program must play matches against four different bots, winning at least 60% of the games in each match.
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>.