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2-Regression/1-Tools/README.md
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@ -10,9 +10,10 @@ Describe what we will learn
### Introduction ### Introduction
Describe what will be covered In this lesson, you will learn:
- How to configure Visual Studio Code for machine learning tasks
> Notes - An introduction to Scikit-Learn, including installation
- Some tips on working with Python for ML
### Prerequisite ### Prerequisite

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In this lesson, you will learn: In this lesson, you will learn:
- Good reasons to create a regression model for a machine learning problem - Good reasons to create a regression model for a machine learning problem
- Some math behind regression models
- Using Scikit-Learn to evaluate your data - Using Scikit-Learn to evaluate your data
- Preparing your data for training, testing, and evaluation - Preparing your data for training, testing, and evaluation

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README.md
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> **A note about quizzes**: All quizzes are contained [in this app](link a quiz app here), 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. > **A note about quizzes**: All quizzes are contained [in this app](link a quiz app here), 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 | | 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 | [📓](1-Introduction/1-intro-to-ML/README.md) | Amy | | 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 | [📓](1-Introduction/2-history-of-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 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Get started with Python and Scikit-Learn for Regression models | [📓](2-Regression/1-Tools/README.md) | Jen | | 03 | North American Pumpkin Prices 🎃 | [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 | [📓](2-Regression/2-Data/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 a Linear Regression model | [📓](2-Regression/3-Linear/README.md) | Jen | | 05 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Linear Regression model | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 06 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Logistic Regression model | [📓](2-Regression/4-Logistic/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 |
| 23 | Future | The Ethics of Machine Learning | What are the important ethical issues apparent now and how will they impact the field going forward? | [📓](8-Future/Ethics/README.md) | | | 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) | |
| 24 | Future | The Future of Machine Learning | What are the important trends that will shape the future of ML? | [📓](8-Future/Future-Trends/README.md) | | | 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) | |
## Offline access ## 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`. 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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