gaspersic/freeCodeCamp
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Feat: add new Markdown parser (#39800)

Browse Source 
and change all the challenges to new `md` format.
This commit is contained in:
Oliver Eyton-Williams
2020-11-27 19:02:05 +01:00
committed by GitHub
parent a07f84c8ec
commit 0bd52f8bd1
2580 changed files with 113436 additions and 111979 deletions
Download Patch File Download Diff File Expand all files Collapse all files

33
curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md
View File

@ -4,49 +4,32 @@ title: Book Recommendation Engine using KNN
challengeType: 10
---
## Description
<section id='description'>
# --description--
In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors.
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/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>.
You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-book-recommendation-engine/blob/master/fcc_book_recommendation_knn.ipynb).
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."
We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
</section>
# --hints--
## Instructions
<section id='instructions'>
It should pass all Python tests.
</section>
## Tests
<section id='tests'>
```yml
tests:
- text: 'It should pass all Python tests.'
testString: ''
```js
```
</section>
# --seed--
## Challenge Seed
<section id='challengeSeed'>
</section>
## Solution
<section id='solution'>
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```
</section>

33
curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md
View File

@ -4,47 +4,30 @@ title: Cat and Dog Image Classifier
challengeType: 10
---
## Description
<section id='description'>
# --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/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>.
You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-cat-and-dog-image-classifier/blob/master/fcc_cat_dog.ipynb).
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."
We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
</section>
# --hints--
## Instructions
<section id='instructions'>
It should pass all Python tests.
</section>
## Tests
<section id='tests'>
```yml
tests:
- text: 'It should pass all Python tests.'
testString: ''
```js
```
</section>
# --seed--
## Challenge Seed
<section id='challengeSeed'>
</section>
## Solution
<section id='solution'>
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```
</section>

33
curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md
View File

@ -4,49 +4,32 @@ title: Linear Regression Health Costs Calculator
challengeType: 10
---
## Description
<section id='description'>
# --description--
In this challenge, you will predict healthcare costs using a regression algorithm.
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/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>.
You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-linear-regression-health-costs-calculator/blob/master/fcc_predict_health_costs_with_regression.ipynb).
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."
We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
</section>
# --hints--
## Instructions
<section id='instructions'>
It should pass all Python tests.
</section>
## Tests
<section id='tests'>
```yml
tests:
- text: 'It should pass all Python tests.'
testString: ''
```js
```
</section>
# --seed--
## Challenge Seed
<section id='challengeSeed'>
</section>
## Solution
<section id='solution'>
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```
</section>

33
curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md
View File

@ -4,47 +4,30 @@ title: Neural Network SMS Text Classifier
challengeType: 10
---
## Description
<section id='description'>
# --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/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>.
You can access [the full project instructions and starter code on Google Colaboratory](https://colab.research.google.com/github/freeCodeCamp/boilerplate-neural-network-sms-text-classifier/blob/master/fcc_sms_text_classification.ipynb).
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."
We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.
</section>
# --hints--
## Instructions
<section id='instructions'>
It should pass all Python tests.
</section>
## Tests
<section id='tests'>
```yml
tests:
- text: 'It should pass all Python tests.'
testString: ''
```js
```
</section>
# --seed--
## Challenge Seed
<section id='challengeSeed'>
</section>
## Solution
<section id='solution'>
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```
</section>

32
curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/rock-paper-scissors.md
View File

@ -4,46 +4,30 @@ title: Rock Paper Scissors
challengeType: 10
---
## Description
<section id='description'>
# --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.
You can access <a href='https://repl.it/github/freeCodeCamp/boilerplate-rock-paper-scissors' target='_blank'>the full project description and starter code on repl.it</a>.
You can access [the full project description and starter code on repl.it](https://repl.it/github/freeCodeCamp/boilerplate-rock-paper-scissors).
After going to that link, fork the project. Once you complete the project based on the instructions in 'README.md', submit your project link below.
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.
</section>
## Instructions
<section id='instructions'>
# --hints--
</section>
It should pass all Python tests.
## Tests
<section id='tests'>
```yml
tests:
- text: 'It should pass all Python tests.'
testString: ''
```js
```
</section>
# --seed--
## Challenge Seed
<section id='challengeSeed'>
</section>
## Solution
<section id='solution'>
# --solutions--
```py
# Python challenges don't need solutions,
# because they would need to be tested against a full working project.
# Please check our contributing guidelines to learn more.
```
</section>