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index a8de4606d0..bfbe589945 100644
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+++ b/3-Web-App/README.md
@@ -4,7 +4,7 @@ In this section of the curriculum, you will be introduced to an applied ML topic
 
 ## Lessons
 
-1. [Build a Web App](1-Web-App/README.md)
+1. [Build a Web App](3-Web-App/README.md)
 
 ## Credits
 
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index 07c03912ec..3fd1cc4c56 100644
--- a/Classification/README.md
+++ b/4-Classification/README.md
@@ -10,4 +10,6 @@ In this section of the curriculum you will learn about how to classify data usin
 4. [Applied ML: Build a Web App](4-Applied/README.md)
 ## Credits
 
-"Getting Started with Classification" was written with ♥️ by [Cassie Breviu](@cassieview)
\ No newline at end of file
+"Getting Started with Classification" was written with ♥️ by [Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper)
+
+The delicious recipes dataset was sourced from [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)
\ No newline at end of file
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@@ -11,7 +11,7 @@ One of the major consumers of classical machine learning models is the finance i
 
 ### Credit card fraud detection
 
-We learned about [k-means clustering](Clustering/2-K-Means/README.md) earlier in the course, but how can it be used to solve problems related to credit card fraud?
+We learned about [k-means clustering](5-Clustering/2-K-Means/README.md) earlier in the course, but how can it be used to solve problems related to credit card fraud?
 
 K-means clustering comes in handy during a credit card fraud detection technique called **outlier detection**. Outliers, or deviations in observations about a set of data, can tell us if a credit card is being used in a normal capacity or if something unusual is going on. As shown in the paper below, you can sort credit card data using a k-means clustering algorithm and assign each transaction to a cluster based on how much of an outlier it appears to be. Then, you can evaluate for riskiest cluster for fraudulent versus legitimate transactions.
 
@@ -20,7 +20,7 @@ https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type
 
 In wealth management, an individual or firm handles investments on behalf of their clients. Their job is to sustain and grow wealth in the long-term, so it is essential to choose investments that perform well.
 
-One way to evaluate how a particular investment performs is through statistical regression. [Linear regression](Regression/1-Tools/README.md) is a valuable tool for understanding how a fund performs relative to some benchmark. We can also deduce whether or not the results of the regression are statistically significant, or how much they would affect a client's investments. You could even further expand your analysis using multiple regression, where additional risk factors can be taken into account. For an example of how this would work for a specific fund, check out the paper below on evaluating fund performance using regression. 
+One way to evaluate how a particular investment performs is through statistical regression. [Linear regression](2-Regression/1-Tools/README.md) is a valuable tool for understanding how a fund performs relative to some benchmark. We can also deduce whether or not the results of the regression are statistically significant, or how much they would affect a client's investments. You could even further expand your analysis using multiple regression, where additional risk factors can be taken into account. For an example of how this would work for a specific fund, check out the paper below on evaluating fund performance using regression. 
 
 http://www.brightwoodventures.com/evaluating-fund-performance-using-regression/
 
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+++ b/README.md
@@ -67,32 +67,32 @@ By ensuring that the content aligns with projects, the process is made more enga
 > **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 |                          Section                           |                  Concepts Taught                  | Learning Objectives                                                                                                             |                    Linked Lesson                    |   Author    |
-| :-----------: | :--------------------------------------------------------: | :-----------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------: | :---------: |
-|      01       |           [Introduction](Introduction/README.md)           |         Introduction to Machine Learning          | Learn the basic concepts behind Machine Learning                                                                                |   [lesson](Introduction/1-intro-to-ML/README.md)    |    Team     |
-|      02       |           [Introduction](Introduction/README.md)           |          The History of Machine Learning          | Learn the history underlying this field                                                                                         |  [lesson](Introduction/2-history-of-ML/README.md)   | Jen and Amy |
-|      03       |           [Introduction](Introduction/README.md)           |           Fairness and Machine Learning           | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](Introduction/3-fairness/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     |
-|      07       |              North American Pumpkin Prices 🎃               |        [Regression](Regression/README.md)         | Build a Logistic Regression model                                                                                               |      [lesson](Regression/4-Logistic/README.md)      |     Jen     |
-|      08       |                        A Web App 🔌                         |           [Web App](Web-App/README.md)            | Build a Web app to use your trained model                                                                                       |             [lesson](Web-App/README.md)             |     Jen     |
-|      09       |               Introduction to Classification               |    [Classification](Classification/README.md)     | Clean, Prep, and Visualize your Data; Introduction to Classification                                                            |      [lesson](Classification/1-Data/README.md)      |   Cassie    |
-|      10       |                 Delicious Asian Recipes 🍜                  |    [Classification](Classification/README.md)     | Build a Discriminative Model                                                                                                    | [lesson](Classification/2-Descriminative/README.md) |   Cassie    |
-|      11       |                 Delicious Asian Recipes 🍜                  |    [Classification](Classification/README.md)     | Build a Generative Model                                                                                                        |   [lesson](Classification/3-Generative/README.md)   |   Cassie    |
-|      12       |                 Delicious Asian Recipes 🍜                  |    [Classification](Classification/README.md)     | Build a Web App using your Model                                                                                                |    [lesson](Classification/4-Applied/README.md)     |     Jen     |
-|      13       |                 Introduction to Clustering                 |        [Clustering](Clustering/README.md)         | Clean, Prep, and Visualize your Data; Introduction to Clustering                                                                |     [lesson](Clustering/1-Visualize/README.md)      |     Jen     |
-|      14       |            Exploring Nigerian Musical Tastes 🎧             |        [Clustering](Clustering/README.md)         | Explore the K-Means Clustering Method                                                                                           |      [lesson](Clustering/2-K-Means/README.md)       |     Jen     |
-|      15       |       Introduction to Natural Language Processing  ☕️       |   [Natural Language Processing](NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](NLP/1-Introduction-to-NLP/README.md)    |   Stephen   |
-|      16       |                     Common NLP Tasks ☕️                     |   [Natural Language Processing](NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](NLP/2-Tasks/README.md)           |   Stephen   |
-|      17       |            Translation and Sentiment Analysis ❤️            |   [Natural Language Processing](NLP/README.md)    | Translation and Sentiment analysis with Jane Austen                                                                             |   [lesson](NLP/3-Translation-Sentiment/README.md)   |   Stephen   |
-|      18       |                Romantic Hotels of Europe ♥️                 |   [Natural Language Processing](NLP/README.md)    | Sentiment analysis, continued                                                                                                   |                     [lesson]()                      |   Stephen   |
-|      19       |                Romantic Hotels of Europe ♥️                 |   [Natural Language Processing](NLP/README.md)    | Sentiment analysis, continued                                                                                                   |                     [lesson]()                      |   Stephen   |
-|      20       |          Introduction to Time Series Forecasting           |        [Time Series](TimeSeries/README.md)        | Introduction to Time Series Forecasting                                                                                         |    [lesson](TimeSeries/1-Introduction/README.md)    |  Francesca  |
-|      21       | ⚡️ World Power Usage ⚡️ Time Series Forecasting with ARIMA ⚡️ |        [Time Series](TimeSeries/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)    |    Team     |
+| Lesson Number |                          Section                           |                   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)    |    Team     |
+|      02       |          [Introduction](1-Introduction/README.md)          |           The History of Machine Learning           | Learn the history underlying this field                                                                                         |   [lesson](Introduction/2-history-of-ML/README.md)    | Jen and Amy |
+|      03       |          [Introduction](1-Introduction/README.md)          |            Fairness and Machine Learning            | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |   Tomomi    |
+|      04       |                 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     |
+|      05       |              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     |
+|      06       |              North American Pumpkin Prices 🎃               |        [Regression](2-Regression/README.md)         | Build Linear and Polynomial Regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |     Jen     |
+|      07       |              North American Pumpkin Prices 🎃               |        [Regression](2-Regression/README.md)         | Build a Logistic Regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |     Jen     |
+|      08       |                        A Web App 🔌                         |           [Web App](3-Web-App/README.md)            | Build a Web app to use your trained model                                                                                       |             [lesson](3-Web-App/README.md)             |     Jen     |
+|      09       |               Introduction to Classification               |    [Classification](4-Classification/README.md)     | Clean, Prep, and Visualize your Data; Introduction to Classification                                                            |      [lesson](4-Classification/1-Data/README.md)      |   Cassie    |
+|      10       |                 Delicious Asian Recipes 🍜                  |    [Classification](4-Classification/README.md)     | Build a Discriminative Model                                                                                                    | [lesson](4-Classification/2-Descriminative/README.md) |   Cassie    |
+|      11       |                 Delicious Asian Recipes 🍜                  |    [Classification](4-Classification/README.md)     | Build a Generative Model                                                                                                        |   [lesson](4-Classification/3-Generative/README.md)   |   Cassie    |
+|      12       |                 Delicious Asian Recipes 🍜                  |    [Classification](4-Classification/README.md)     | Build a Web App using your Model                                                                                                |    [lesson](4-Classification/4-Applied/README.md)     |     Jen     |
+|      13       |                 Introduction to Clustering                 |        [Clustering](5-Clustering/README.md)         | Clean, Prep, and Visualize your Data; Introduction to Clustering                                                                |     [lesson](5-Clustering/1-Visualize/README.md)      |     Jen     |
+|      14       |            Exploring Nigerian Musical Tastes 🎧             |        [Clustering](5-Clustering/README.md)         | Explore the K-Means Clustering Method                                                                                           |      [lesson](5-Clustering/2-K-Means/README.md)       |     Jen     |
+|      15       |       Introduction to Natural Language Processing  ☕️       |   [Natural Language Processing](6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](6-NLP/1-Introduction-to-NLP/README.md)    |   Stephen   |
+|      16       |                     Common NLP Tasks ☕️                     |   [Natural Language Processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](6-NLP/2-Tasks/README.md)           |   Stephen   |
+|      17       |            Translation and Sentiment Analysis ❤️            |   [Natural Language Processing](6-NLP/README.md)    | Translation and Sentiment analysis with Jane Austen                                                                             |   [lesson](6-NLP/3-Translation-Sentiment/README.md)   |   Stephen   |
+|      18       |                Romantic Hotels of Europe ♥️                 |   [Natural Language Processing](6-NLP/README.md)    | Sentiment analysis, continued                                                                                                   |                      [lesson]()                       |   Stephen   |
+|      19       |                Romantic Hotels of Europe ♥️                 |   [Natural Language Processing](6-NLP/README.md)    | Sentiment analysis, continued                                                                                                   |                      [lesson]()                       |   Stephen   |
+|      20       |          Introduction to Time Series Forecasting           |        [Time Series](7-TimeSeries/README.md)        | Introduction to Time Series Forecasting                                                                                         |    [lesson](7-TimeSeries/1-Introduction/README.md)    |  Francesca  |
+|      21       | ⚡️ World Power Usage ⚡️ Time Series Forecasting with ARIMA ⚡️ |        [Time Series](7-TimeSeries/README.md)        | Time Series Forecasting with ARIMA                                                                                              |       [lesson](7-TimeSeries/2-ARIMA/README.md)        |  Francesca  |
+|      22       |           Introduction to Reinforcement Learning           | [Reinforcement Learning](8-Reinforcement/README.md) | tbd                                                                                                                             |                      [lesson]()                       |   Dmitry    |
+|      23       |                Help Peter avoid the Wolf! 🐺                | [Reinforcement Learning](8-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](9-Real-World/1-Applications/README.md)    |    Team     |
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