diff --git a/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md b/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md index a7f9edecaf..e52ff23a55 100644 --- a/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md +++ b/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md @@ -9,7 +9,7 @@ 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 the full project instructions and starter code on Google Colaboratory. +You can access the full project instructions and starter code on Google Colaboratory. 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." diff --git a/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md b/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md index 7a52c4ec9b..cbf6d64c8e 100644 --- a/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md +++ b/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md @@ -7,7 +7,7 @@ challengeType: 10
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 the full project instructions and starter code on Google Colaboratory. +You can access the full project instructions and starter code on Google Colaboratory. 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." diff --git a/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md b/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md index d8c3e9172a..2586dcdb16 100644 --- a/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md +++ b/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md @@ -9,7 +9,7 @@ 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 the full project instructions and starter code on Google Colaboratory. +You can access the full project instructions and starter code on Google Colaboratory. 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." diff --git a/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md b/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md index f0ff086986..8afbc82da0 100644 --- a/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md +++ b/curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md @@ -7,7 +7,7 @@ challengeType: 10
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 the full project instructions and starter code on Google Colaboratory. +You can access the full project instructions and starter code on Google Colaboratory. 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." diff --git a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md index 93d2281852..8aac05670c 100644 --- a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md +++ b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md @@ -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 the full project instructions and starter code on Google Colaboratory. +You can access the full project instructions and starter code on Google Colaboratory. 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." diff --git a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md index 5fca74b415..85f5f682e1 100644 --- a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md +++ b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md @@ -8,7 +8,7 @@ challengeType: 10
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 the full project instructions and starter code on Google Colaboratory. +You can access the full project instructions and starter code on Google Colaboratory. 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." diff --git a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md index 6909ac00e6..7a14567216 100644 --- a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md +++ b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md @@ -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 the full project instructions and starter code on Google Colaboratory. +You can access the full project instructions and starter code on Google Colaboratory. 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." diff --git a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md index 544498e536..d21b5a4c5f 100644 --- a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md +++ b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md @@ -8,7 +8,7 @@ challengeType: 10
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 the full project instructions and starter code on Google Colaboratory. +You can access the full project instructions and starter code on Google Colaboratory. 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."