intro image

babysor · Jun 17, 2021 · 9451429 · 9451429
1 parent 5b8cd8c
commit 9451429
Show file tree

Hide file tree

Showing 3 changed files with 13 additions and 9 deletions.
diff --git a/1-Introduction/README.md b/1-Introduction/README.md
@@ -1,6 +1,10 @@
-# Introduction to Machine Learning
+# Introduction to machine learning
 
-In this section of the curriculum, you will be introduced to the base concepts underlying the field of machine learning, what it is, and learn about its history and the techniques researchers use to work with it.
+In this section of the curriculum, you will be introduced to the base concepts underlying the field of machine learning, what it is, and learn about its history and the techniques researchers use to work with it.  Let's explore this new world of ML together!
+
+![globe](images/globe.jpg)
+> Photo by <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> on <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
 ### Lessons
 
 1. [Introduction to Machine Learning](1-intro-to-ML/README.md)

diff --git a/1-Introduction/images/globe.jpg b/1-Introduction/images/globe.jpg
diff --git a/README.md b/README.md
@@ -69,16 +69,16 @@ 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     |
+| Lesson Number |                         Topic                          |                   Section                   | 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)    |    Muhammad    |
-|      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](1-Introduction/README.md)         |           Techniques for machine learning           | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
+|      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   |
+|      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       |
+|      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       |
 |      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 |
@@ -91,7 +91,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 |      18       |           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     |
 |      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    |
+|      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) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
 |      23       |               Help Peter avoid the wolf! 🐺               | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
 |      24       |         Real-World ML scenarios and applications         |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](9-Real-World/1-Applications/README.md)    |      Team      |