lesson details on regions

walbercardoso · Jun 6, 2021 · 8f1770f · 8f1770f
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diff --git a/2-Regression/README.md b/2-Regression/README.md
@@ -1,5 +1,5 @@
 # Regression Models for Machine Learning
-## Regional topic: Regression models for pumpkin prices in North America
+## Regional topic: Regression models for pumpkin prices in North America 🎃
 
 In North America, pumpkins are often carved into scary faces for Halloween. Let's discover more about these fascinating vegetables!
 

diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
@@ -1,6 +1,7 @@
 # Introduction to Classification
 
 [![Introduction to Classification](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to Classification")
+
 > 🎥 Click the image above for a video: MIT's John Guttag introduces Classification
 
 ## [Pre-lecture quiz](link-to-quiz-app)

diff --git a/4-Classification/README.md b/4-Classification/README.md
@@ -1,7 +1,15 @@
 # Getting Started with Classification
+## Regional topic: Delicious Asian Recipes 🍜
 
-In this section of the curriculum you will learn about how to classify data using Machine Learning. 
+In Asia, food traditions are extremely diverse, and very delicious! Let's look at data about regional recipes to try to guess where they originated.
 
+![Thai food seller](./images/thai-food.jpg)
+> Photo by <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> on <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+
+## What you will learn
+
+In this section, you will build on the skills you learned in Lesson 1 (Regression) to learn about more classifiers you can use that will help you learn about your data.
 ## Lessons
 
 1. [Introduction to Classification](1-Introduction/README.md)

diff --git a/4-Classification/images/thai-food.jpg b/4-Classification/images/thai-food.jpg
diff --git a/5-Clustering/README.md b/5-Clustering/README.md
@@ -1,5 +1,5 @@
 # Clustering Models for Machine Learning
-## Regional topic: Clustering models for a Nigerian audience's musical taste
+## Regional topic: Clustering models for a Nigerian audience's musical taste 🎧
 
 Nigeria's diverse audience has diverse musical tastes. Using data scraped from Spotify (inspired by [this article](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421), let's look at some music popular in Nigeria. This dataset includes data about various songs' 'danceability' score, 'acousticness', loudness, 'speechiness', popularity and energy. It will be interesting to discover patterns in this data!
 

diff --git a/6-NLP/README.md b/6-NLP/README.md
@@ -1,5 +1,7 @@
 # Getting Started with Natural Language Processing 
 
+## Regional topic: European literature and Romantic Hotels of Europe ❤️ 
+
 In this section of the curriculum, you will be introduced to one of the most widespread uses of machine learning: Natural Language Processing (NLP). Derived from Computational Linguistics, this category of Artificial Intelligence is the bridge between humans and machines via voice or textual communication. 
 
 In these lessons we'll learn the basics of NLP by building small conversational bots to learn how Machine Learning aids in making these conversations more and more 'smart'. You'll travel back in time, chatting with Elizabeth Bennett and Mr. Darcy from Jane Austen's classic novel, **Pride and Prejudice**, published in 1813. Then, you'll further your knowledge by learning about sentiment analysis via hotel reviews in Europe.

diff --git a/7-TimeSeries/README.md b/7-TimeSeries/README.md
@@ -1,5 +1,7 @@
 # Time Series Forecasting
 
+## Regional topic: Worldwide Electricity Usage ✨
+
 In these two lessons, you will be introduced to Time Series Forecasting, a somewhat lesser known area of Machine Learning that is nevertheless extremely valuable for industry and business applications, among other fields. While neural networks can be used to enhance the utility of these models, we will study them in the context of classical machine learning as models help predict future performance based on the past.
 
 Our regional focus is electrical usage in the world, an interesting dataset to learn about forecasting future power usage based on patterns of past load. You can see how this kind of forecasting can be extremely helpful in a business environment.

diff --git a/9-Real-World/README.md b/9-Real-World/README.md
@@ -4,7 +4,6 @@ In this section of the curriculum, you will be introduced to some real-world app
 ## Lesson
 
 1. [Real-World Applications for ML](1-Applications/README.md)
-
 ## Credits
 
 "Real-World Applications" was written by a team of folks, including [Jen Looper](https://twitter.com/jenlooper), [Ornella Altunyan](https://twitter.com/ornelladotcom) and ...