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fixing the order of classification/clustering

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Jen Looper 4 years ago
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3-Classification/README.md
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# Getting Started with
# Getting Started with Classification
In this section of the curriculum, you will be introduced to ...
In this section of the curriculum you will learn about how to classify data using Machine Learning.
## Topics
1. [Introduction to](1-intro-to/README.md)
1. [Visualize your Data and Prepare it for Use](1-Data/README.md)
2. [Build a Discriminative Model](2-Discriminative/README.md)
3. [Build a Generative Model](3-Generative/README.md)
4. [Applied ML: Build a Web App](4-Applied/README.md)
## Credits
"Introduction to" was written with ♥️ by [Name](Twitter)
"Getting Started with Classification" was written with ♥️ by [Cassie Breviu](@cassieview)

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README.md
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> **A note about quizzes**: All quizzes are contained [in this app](https://jolly-sea-0a877260f.azurestaticapps.net), for 48 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
| Lesson Number | Project Name/Group | Concepts Taught | Learning Objectives | Linked Lesson | Author |
| :-----------: | :--------------------------------------: | :----------------------------------: | ---------------------------------------------------------------------------------------------------- | :------------------------------------------------: | :-------: |
| 01 | [Introduction](1-Introduction/README.md) | Introduction to Machine Learning | Learn the basic concepts behind Machine Learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Amy |
| 02 | [Introduction](1-Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](1-Introduction/2-history-of-ML/README.md) | Amy |
| 03 | Introduction to Regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-Learn for Regression models | [lesson](2-Regression/1-Tools/README.md) | Jen |
| 04 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](2-Regression/2-Data/README.md) | Jen |
| 05 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build Linear and Polynomial Regression models | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 06 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Logistic Regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen |
| 07 | Introduction to Clustering | [Clustering]() | tbd | [lesson]() | Cassie |
| 08 | Delicious Asian Recipes 🍜 | [Clustering]() | tbd | [lesson]() | Cassie |
| 09 | Delicious Asian Recipes 🍜 | [Clustering]() | tbd | [lesson]() | Cassie |
| 10 | Delicious Asian Recipes 🍜 | [Clustering]() | tbd | [lesson]() | Cassie |
| 11 | Introduction to Classification | [Classification]() | | [lesson]() | Paige |
| 12 | Interesting Maya Architecture 🦜 | [Classification]() | | [lesson]() | Paige |
| 13 | Interesting Maya Architecture 🦜 | [Classification]() | | [lesson]() | Paige |
| 14 | Interesting Maya Architecture 🦜 | [Classification]() | | [lesson]() | Paige |
| 15 | Introduction to NLP | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 16 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 17 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 18 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 19 | Introduction to Time Series Forecasting | [Time Series]() | tbd | [lesson]() | Francesca |
| 20 | Power Usage in India ⚡️ | [Time Series]() | tbd | [lesson]() | Francesca |
| 21 | Introduction to Reinforcement Learning | [Reinforcement Learning]() | tbd | [lesson]() | Dmitry |
| 22 | A fancy French mousetrap 🍫 | [Reinforcement Learning]() | tbd | [lesson]() | Dmitry |
| 23 | Future | The Ethics of Machine Learning | What are the important ethical issues apparent now and how will they impact the field going forward? | [lesson](8-Future/Ethics/README.md) | Tomomi |
| 24 | Future | The Future of Machine Learning | What are the important trends that will shape the future of ML? | [lesson](8-Future/Future-Trends/README.md) | All |
| Lesson Number | Project Name/Group | Concepts Taught | Learning Objectives | Linked Lesson | Author |
| :-----------: | :--------------------------------------: | :------------------------------------------: | ---------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :-------: |
| 01 | [Introduction](1-Introduction/README.md) | Introduction to Machine Learning | Learn the basic concepts behind Machine Learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Amy |
| 02 | [Introduction](1-Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](1-Introduction/2-history-of-ML/README.md) | Amy |
| 03 | Introduction to Regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-Learn for Regression models | [lesson](2-Regression/1-Tools/README.md) | Jen |
| 04 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](2-Regression/2-Data/README.md) | Jen |
| 05 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build Linear and Polynomial Regression models | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 06 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Logistic Regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen |
| 07 | Introduction to Classification | [Classification](3-Classification/README.md) | Clean, Prep, and Visualize your Data; Introduction to Classification | [lesson](3-Classification/1-Data/README.md) | Cassie |
| 08 | Delicious Asian Recipes 🍜 | [Classification](3-Classification/README.md) | Build a Discriminative Model | [lesson](3-Classification/2-Descriminative/README.md) | Cassie |
| 09 | Delicious Asian Recipes 🍜 | [Classification](3-Classification/README.md) | Build a Generative Model | [lesson](3-Classification/3-Generative/README.md) | Cassie |
| 10 | Delicious Asian Recipes 🍜 | [Classification](3-Classification/README.md) | Build a Web App using your Model | [lesson](3-Classification/4-Applied/README.md) | Cassie |
| 11 | Introduction to Clustering | [Clustering](4-Clustering/README.md) | | [lesson]() | Paige |
| 12 | Interesting Maya Architecture 🦜 | [Clustering]() | | [lesson]() | Paige |
| 13 | Interesting Maya Architecture 🦜 | [Clustering]() | | [lesson]() | Paige |
| 14 | Interesting Maya Architecture 🦜 | [Clustering]() | | [lesson]() | Paige |
| 15 | Introduction to NLP | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 16 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 17 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 18 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 19 | Introduction to Time Series Forecasting | [Time Series]() | tbd | [lesson]() | Francesca |
| 20 | Power Usage in India ⚡️ | [Time Series]() | tbd | [lesson]() | Francesca |
| 21 | Introduction to Reinforcement Learning | [Reinforcement Learning]() | tbd | [lesson]() | Dmitry |
| 22 | A fancy French mousetrap 🍫 | [Reinforcement Learning]() | tbd | [lesson]() | Dmitry |
| 23 | Future | The Ethics of Machine Learning | What are the important ethical issues apparent now and how will they impact the field going forward? | [lesson](8-Future/Ethics/README.md) | Tomomi |
| 24 | Future | The Future of Machine Learning | What are the important trends that will shape the future of ML? | [lesson](8-Future/Future-Trends/README.md) | All |
## Offline access
You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.

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