From 981cbaaf0c5bf345b3293b88204ee12eec91fdcf Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Wed, 23 Jun 2021 19:48:45 -0400 Subject: [PATCH] fixing links --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 7efe6efd2..89c0f15ef 100644 --- a/README.md +++ b/README.md @@ -72,14 +72,14 @@ By ensuring that the content aligns with projects, the process is made more enga | Lesson Number | Topic | Section | Learning Objectives | Linked Lesson | Author | | :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: | | 01 | Introduction to machine learning | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind machine learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad | -| 02 | The History of machine learning | [Introduction](1-Introduction/README.md) | Learn the history underlying this field | [lesson](Introduction/2-history-of-ML/README.md) | Jen and Amy | +| 02 | The History of machine learning | [Introduction](1-Introduction/README.md) | Learn the history underlying this field | [lesson](1-Introduction/2-history-of-ML/README.md) | Jen and Amy | | 03 | Fairness and machine learning | [Introduction](1-Introduction/README.md) | 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 | 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 | [lesson](2-Regression/1-Tools/README.md) | Jen | | 06 | 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 | | 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | [lesson](2-Regression/3-Linear/README.md) | Jen | | 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen | -| 09 | 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 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a web app to use your trained model | [lesson](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 | [lesson](4-Classification/1-Introduction/README.md) | Jen and Cassie | | 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Introduction to classifiers | [lesson](4-Classification/2-Classifiers-1/README.md) | Jen and Cassie | | 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | More classifiers | [lesson](4-Classification/3-Classifiers-2/README.md) | Jen and Cassie |