edits for web app assignment and time series lesson renaming

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Jen Looper 3 years ago
parent 62848fa43f
commit b219b206e8

@ -78,12 +78,12 @@ By ensuring that the content aligns with projects, the process is made more enga
| 13 | Introduction to Clustering | [Clustering](Clustering/README.md) | Clean, Prep, and Visualize your Data; Introduction to Clustering | [lesson](Clustering/1-Visualize/README.md) | |
| 14 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](Clustering/README.md) | Explore the K-Means Clustering Method | [lesson](Clustering/2-K-Means/README.md) | |
| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](Clustering/README.md) | Explore Centroid models for Clustering | [lesson](Clustering/3-Centroid/README.md) | |
| 16 | Introduction to Natural Language Processing | [Natural Language Processing](NLP/README.md) | tbd | [lesson]() | Stephen |
| 16 | Introduction to Natural Language Processing | [Natural Language Processing](NLP/README.md) | tbd | [lesson]() | Stephen |
| 17 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](NLP/README.md) | tbd | [lesson]() | Stephen |
| 18 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](NLP/README.md) | tbd | [lesson]() | Stephen |
| 19 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](NLP/README.md) | tbd | [lesson]() | Stephen |
| 20 | Introduction to Time Series Forecasting | [Time Series](Time-Series/README.md) | Introduction to Time Series Forecasting | [lesson]() | Francesca |
| 21 | ⚡️ World Power Usage ⚡️ Time Series Forecasting with ARIMA ⚡️ | [Time Series](Time-Series/README.md) | Time Series Forecasting with ARIMA | [lesson]() | Francesca |
| 20 | Introduction to Time Series Forecasting | [Time Series](Time-Series/README.md) | Introduction to Time Series Forecasting | [lesson](Time-Series/1-Introduction/README.md) | Francesca |
| 21 | ⚡️ World Power Usage ⚡️ Time Series Forecasting with ARIMA ⚡️ | [Time Series](Time-Series/README.md) | Time Series Forecasting with ARIMA | [lesson](TimeSeries/2-ARIMA/README.md) | Francesca |
| 22 | Introduction to Reinforcement Learning | [Reinforcement Learning](Reinforcement/README.md) | tbd | [lesson]() | Dmitry |
| 23 | Help Peter avoid the Wolf! 🐺 | [Reinforcement Learning](Reinforcement/README.md) | tbd | [lesson]() | Dmitry |
| 24 | Real-World ML Scenarios and Applications | ML in the Wild | Interesting and Revealing real-world applications of classical ML | [lesson](Real-World/1-Applications/README.md) | All |

@ -268,6 +268,6 @@ Using a model this way, with Flask and a pickled model, is relatively straightfo
## Review & Self Study
**Assignment**: [Assignment Name](assignment.md)
**Assignment**: [Try a different model](assignment.md)

@ -1 +1,11 @@
# Assignment
# Try a different model
## Instructions
Now that you have built one web app using a trained Regression model, use one of the models from an earlier Regression lesson to redo this web app. You can keep the style or design it differently to reflect the pumpkin data. Be careful to change the inputs to reflect your model's training method.
## Rubric
| Criteria | Exemplary | Adequate | Needs Improvement |
| -------------------------- | --------------------------------------------------------- | --------------------------------------------------------- | -------------------------------------- |
| A new web app is presented | The web app runs as expected and is deployed to the cloud | The web app contains flaws or exhibits unexpected results | The web app does not function properly |

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