Merge branch 'main' of https://github.com/jlooper/ml-for-beginners into main

README.md

@@ -65,7 +65,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 | :-----------: | :--------------------------------------------------------: | :-----------------------------------------------: | --------------------------------------------------------------------------------------------------------- | :-------------------------------------------------: | :-------: |
 |      01       |           [Introduction](Introduction/README.md)           |         Introduction to Machine Learning          | Learn the basic concepts behind Machine Learning                                                          |   [lesson](Introduction/1-intro-to-ML/README.md)    |    Amy    |
 |      02       |           [Introduction](Introduction/README.md)           |          The History of Machine Learning          | Learn the history underlying this field                                                                   |  [lesson](Introduction/2-history-of-ML/README.md)   |    Amy    |
-|      03       |           [Introduction](Introduction/README.md)           |          The Ethics of Machine Learning           | What are the important ethical issues that students should consider when building and applying ML models? |      [lesson](Introduction/3-Ethics/README.md)      |  Tomomi   |
+|      03       |           [Introduction](Introduction/README.md)           |          The Ethics of Machine Learning           | What are the important ethical issues that students should consider when building and applying ML models? |      [lesson](Introduction/3-ethics/README.md)      |  Tomomi   |
 |      04       |                 Introduction to Regression                 |        [Regression](Regression/README.md)         | Get started with Python and Scikit-Learn for Regression models                                            |       [lesson](Regression/1-Tools/README.md)        |    Jen    |
 |      05       |              North American Pumpkin Prices 🎃               |        [Regression](Regression/README.md)         | Visualize and clean data in preparation for ML                                                            |        [lesson](Regression/2-Data/README.md)        |    Jen    |
 |      06       |              North American Pumpkin Prices 🎃               |        [Regression](Regression/README.md)         | Build Linear and Polynomial Regression models                                                             |       [lesson](Regression/3-Linear/README.md)       |    Jen    |
@@ -86,7 +86,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 |      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    |
+|      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)    |    Ornella    |
 ## 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`.

Real-World/1-Applications/README.md

@@ -33,6 +33,8 @@ One of the major consumers of classical machine learning models is the finance i
 
 ### Forest management
 ### Motion sensing of animals
+### Energy Management
+  This article discusses in detail how clustering and time series forecasting help predict future energy use in Ireland, based off of smart metering: https://www-cdn.knime.com/sites/default/files/inline-images/knime_bigdata_energy_timeseries_whitepaper.pdf
 
 ## Insurance