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fix(learn): update machine learning project colab links (#40213)

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* fix(learn): update machine learning project colab links

Update current machine learning project Colab links to clone Jupyter notebooks from the boilerplate repos rather than the ones in Google Drive.

* fix(learn): update chinese versions of colab links
This commit is contained in:
Kristofer Koishigawa
2020-11-12 01:56:23 +09:00
committed by GitHub
parent 5aef8a70c6
commit 135928dc5d
8 changed files with 8 additions and 8 deletions
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2
curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md
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@ -10,7 +10,7 @@ In this challenge, you will create a book recommendation algorithm using K-Neare
You will use the Book-Crossings dataset. This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.
You can access <a href='https://colab.research.google.com/drive/1TDgXyXqZwsiGlnuF-bmQ2Rh3x5NcrHEn' target='_blank'>the full project instructions and starter code on Google Colaboratory</a>.
You can access <a href='https://colab.research.google.com/github/freeCodeCamp/boilerplate-book-recommendation-engine/blob/master/fcc_book_recommendation_knn.ipynb' target='_blank'>the full project instructions and starter code on 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/cat-and-dog-image-classifier.md
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@ -8,7 +8,7 @@ challengeType: 10
<section id='description'>
For this challenge, you will use TensorFlow 2.0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63% accuracy.
You can access <a href='https://colab.research.google.com/drive/1UCHiRuBLxo0S3aMuiDXlaP54LsxzrXHz' target='_blank'>the full project instructions and starter code on Google Colaboratory</a>.
You can access <a href='https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb' target='_blank'>the full project instructions and starter code on 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."

2
curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md
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@ -10,7 +10,7 @@ In this challenge, you will predict healthcare costs using a regression algorith
You are given a dataset that contains information about different people including their healthcare costs. Use the data to predict healthcare costs based on new data.
You can access <a href='https://colab.research.google.com/drive/1o8sTSCMa8Tnmcqhp_2BKKJEaHFoFmRzI?usp=sharing' target='_blank'>the full project instructions and starter code on Google Colaboratory</a>.
You can access <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'>the full project instructions and starter code on 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 @@ challengeType: 10
<section id='description'>
In this challenge, you need to create a machine learning model that will classify SMS messages as either "ham" or "spam". A "ham" message is a normal message sent by a friend. A "spam" message is an advertisement or a message sent by a company.
You can access <a href='https://colab.research.google.com/drive/1qfVQwSKAKU-NKPY4ByBhr93EqSqds4dJ' target='_blank'>the full project instructions and starter code on Google Colaboratory</a>.
You can access <a href='https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb' target='_blank'>the full project instructions and starter code on 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."