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chore(curriculum): strandize links in projects (#46465)

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Muhammed Mustafa
2022-06-13 12:29:31 +02:00
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curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md
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@@ -8,7 +8,7 @@ dashedName: book-recommendation-engine-using-knn
# --description--
You will be [working on this project with Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-book-recommendation-engine/blob/master/fcc_book_recommendation_knn.ipynb).
You will be <a href="https://colab.research.google.com/github/freeCodeCamp/boilerplate-book-recommendation-engine/blob/master/fcc_book_recommendation_knn.ipynb" target="_blank" rel="noopener noreferrer nofollow">working on this project with Google Colaboratory</a>.
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."
@@ -18,7 +18,7 @@ We are still developing the interactive instructional content for the machine le
In this challenge, you will create a book recommendation algorithm using **K-Nearest Neighbors**.
You will use the [Book-Crossings dataset](http://www2.informatik.uni-freiburg.de/~cziegler/BX/). This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.
You will use the <a href="http://www2.informatik.uni-freiburg.de/~cziegler/BX/" target="_blank" rel="noopener noreferrer nofollow">Book-Crossings dataset</a>. This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.
After importing and cleaning the data, use `NearestNeighbors` from `sklearn.neighbors` to develop a model that shows books that are similar to a given book. The Nearest Neighbors algorithm measures the distance to determine the “closeness” of instances.

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curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md
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@@ -8,7 +8,7 @@ dashedName: cat-and-dog-image-classifier
# --description--
You will be [working on this project with Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb).
You will be <a href="https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb" target="_blank" rel="noopener noreferrer nofollow">working on this project with Google Colaboratory</a>.
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/english/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 [working on this project with Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb).
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>.
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/english/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 [working on this project with Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb).
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>.
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."
@@ -20,7 +20,7 @@ In this challenge, you need to create a machine learning model that will classif
You should create a function called `predict_message` that takes a message string as an argument and returns a list. The first element in the list should be a number between zero and one that indicates the likeliness of "ham" (0) or "spam" (1). The second element in the list should be the word "ham" or "spam", depending on which is most likely.
For this challenge, you will use the [SMS Spam Collection](http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/) dataset. The dataset has already been grouped into train data and test data.
For this challenge, you will use the <a href="http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/" target="_blank" rel="noopener noreferrer nofollow">SMS Spam Collection</a> dataset. The dataset has already been grouped into train data and test data.
The first two cells import the libraries and data. The final cell tests your model and function. Add your code in between these cells.

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curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/rock-paper-scissors.md
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@@ -10,7 +10,7 @@ dashedName: rock-paper-scissors
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.
You will be [working on this project with our Replit starter code](https://replit.com/github/freeCodeCamp/boilerplate-rock-paper-scissors).
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>.
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.