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chore(i18n,chn): manually downloaded curriculum (#42858)

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curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md
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---
id: 5e46f8e3ac417301a38fb92f
title: Book Recommendation Engine using KNN
title: 基于 KNN 的图书推荐引擎
challengeType: 10
forumTopicId: 462378
dashedName: book-recommendation-engine-using-knn
---
# --description--
In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors.
在这个挑战中，你将使用 K-近邻算法创建一个书籍推荐算法。
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.
你将使用 Book-Crossings 数据集。 该数据集包括 90,000 名用户对 270,000 册书籍的 110 万份评分（评分从 1至 10）。
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).
你可以[在 Replit 上查看整个项目的具体描述和初始代码](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."
进入该链接后，在你自己的账户或本地创建一个笔记本的副本。 一旦你完成项目并通过链接中的测试，请在下面提交你的项目链接。 如果你提交的是 Google Colaboratory 的链接，请确保打开链接共享时选择 “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.
我们仍在开发机器学习课程的交互式课程部分。 现在，你可以通过这个认证视频挑战。 你可能还需要寻找额外的学习资源，类似于你在现实世界项目上所做的工作。
# --hints--
It should pass all Python tests.
它应该通过所有的 Python 测试。
```js

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curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md
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---
id: 5e46f8dcac417301a38fb92e
title: Cat and Dog Image Classifier
title: 猫狗图像分类器
challengeType: 10
forumTopicId: 462377
dashedName: cat-and-dog-image-classifier
---
# --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.
在这个挑战中，你将使用 TensorFlow 2.0 和 Keras 创建一个卷积神经网络，对猫和狗的图像进行正确分类，准确率至少达到 63%。
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).
你可以访问 [Google Colaboratory 上的完整项目说明和启动代码](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."
进入该链接后，在你自己的账户或本地创建一个笔记本的副本。 一旦你完成项目并通过链接中的测试，请在下面提交你的项目链接。 如果你提交的是 Google Colaboratory 的链接，请确保打开链接共享时选择 “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.
我们仍在开发机器学习课程的交互式课程部分。 现在，你可以通过这个认证视频挑战。 你可能还需要寻找额外的学习资源，类似于你在真实世界项目中的工作。
# --hints--
It should pass all Python tests.
它应该通过所有的 Python 测试。
```js

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curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/linear-regression-health-costs-calculator.md
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---
id: 5e46f8edac417301a38fb930
title: Linear Regression Health Costs Calculator
title: 线性回归健康成本计算器
challengeType: 10
forumTopicId: 462379
dashedName: linear-regression-health-costs-calculator
---
# --description--
In this challenge, you will predict healthcare costs using a regression algorithm.
在这个挑战中，你将使用回归算法预测医疗费用。
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.
你会得到一个数据集，其中包含不同人的信息，包括他们的医疗费用。 用数据来预测基于新数据的医疗费用。
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).
你可以访问 [Google Colaboratory 上的完整项目说明和启动代码](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."
进入该链接后，在你自己的账户或本地创建一个笔记本的副本。 一旦你完成项目并通过链接中的测试，请在下面提交你的项目链接。 如果你提交的是 Google Colaboratory 的链接，请确保打开链接共享时选择 “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.
我们仍在开发机器学习课程的交互式课程部分。 现在，你可以通过这个认证中的视频挑战。 你可能还需要寻找额外的学习资源，类似于你在真实世界项目中的工作。
# --hints--
It should pass all Python tests.
它应该通过所有的 Python 测试。
```js

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curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/neural-network-sms-text-classifier.md
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---
id: 5e46f8edac417301a38fb931
title: Neural Network SMS Text Classifier
title: 神经网络短信文本分类器
challengeType: 10
forumTopicId: 462380
dashedName: neural-network-sms-text-classifier
---
# --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.
在这个挑战中，你需要创建一个机器学习模型，将短信分类为 “ham” 或 “spam”。 “ham” 信息是由一个朋友发送的普通信息。 “spam” 是一个公司发送的广告或信息。
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).
你可以访问 [Google Colaboratory 上的完整项目说明和启动代码](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."
进入该链接后，在你自己的账户或本地创建一个笔记本的副本。 一旦你完成项目并通过链接中的测试，请在下面提交你的项目链接。 如果你提交的是 Google Colaboratory 的链接，请确保打开链接共享时选择 “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.
我们仍在开发机器学习课程的交互式课程部分。 现在，你可以通过这个认证中的视频挑战。 你可能还需要寻找额外的学习资源，类似于你在真实世界项目中的工作。
# --hints--
It should pass all Python tests.
它应该通过所有的 Python 测试。
```js

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curriculum/challenges/chinese/11-machine-learning-with-python/machine-learning-with-python-projects/rock-paper-scissors.md
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---
id: 5e46f8d6ac417301a38fb92d
title: Rock Paper Scissors
title: 剪刀石头布
challengeType: 10
forumTopicId: 462376
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.
在这个挑战中，你将创建一个程序来玩石头、剪刀、布。 一个随机选取的程序通常会有 50% 的时间获胜。 要通过这一挑战，你的程序必须与四个不同的机器人进行对战，并达到至少 60% 胜率。
You can access [the full project description and starter code on repl.it](https://repl.it/github/freeCodeCamp/boilerplate-rock-paper-scissors).
你可以访问 [Replit 上的完整项目描述和启动代码](https://replit.com/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.
进入该链接后，fork 该项目。 一旦你根据 “README.md” 中的说明完成了项目，请在下面提交你的项目链接。
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.
我们仍在开发机器学习课程的交互式课程部分。 现在，你需要使用其他资源来学习如何通过这一挑战。
# --hints--
It should pass all Python tests.
它应该通过所有的 Python 测试。
```js