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| 04 | Techniques for machine learning | [Introduction](1-Introduction/README.md) | What techniques do ML researchers use to build ML models? | [Lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | Introduction to regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-learn for regression models |
- [Python](2-Regression/1-Tools/README.md)
- [R](2-Regression/1-Tools/solution/R/lesson_1-R.ipynb)
| |
| 06 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | - [Python](2-Regression/2-Data/README.md)
- [R](2-Regression/2-Data/solution/R/lesson_2-R.ipynb)
| |
-| 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | - [Python](2-Regression/3-Linear/README.md)
- [R](2-Regression/3-Linear/solution/R/lesson_3-R.ipynb)
| |
+| 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | - [Python](2-Regression/3-Linear/README.md)
- [R](2-Regression/3-Linear/solution/R/lesson_3-R.ipynb)
| - Jen and Dmitry
- Eric Wanjau
|
| 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model | - [Python](2-Regression/4-Logistic/README.md)
- [R](2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb)
| |
| 09 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a web app to use your trained model | [Python](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Introduction to classification | [Classification](4-Classification/README.md) | Clean, prep, and visualize your data; introduction to classification | - [Python](4-Classification/1-Introduction/README.md)
- [R](4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb) |
- Jen and Cassie
- Eric Wanjau
|