capitalization final audit

1-Introduction/README.md

@@ -7,11 +7,10 @@ In this section of the curriculum, you will be introduced to the base concepts u
   
 ### Lessons
 
-1. [Introduction to Machine Learning](1-intro-to-ML/README.md)
-1. [The History of Machine Learning and AI](2-history-of-ML/README.md)
-1. [Fairness and Machine Learning](3-fairness/README.md)
-1. [Techniques of Machine Learning](4-techniques-of-ML/README.md)
-
+1. [Introduction to machine learning](1-intro-to-ML/README.md)
+1. [The History of machine learning and AI](2-history-of-ML/README.md)
+1. [Fairness and machine learning](3-fairness/README.md)
+1. [Techniques of machine learning](4-techniques-of-ML/README.md)
 ### Credits
 
 "Introduction to Machine Learning" was written with ♥️ by a team of folks including [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) and [Jen Looper](https://twitter.com/jenlooper)

2-Regression/README.md

@@ -1,4 +1,4 @@
-# Regression Models for Machine Learning
+# Regression models for machine learning
 ## 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!
@@ -8,25 +8,25 @@ In North America, pumpkins are often carved into scary faces for Halloween. Let'
   
 ## What you will learn
 
-The lessons in this section cover types of Regression in the context of machine learning. Regression models can help determine the _relationship_ between variables. This type of model can predict values such as length, temperature, or age, thus uncovering relationships between variables as it analyzes data points.
+The lessons in this section cover types of regression in the context of machine learning. Regression models can help determine the _relationship_ between variables. This type of model can predict values such as length, temperature, or age, thus uncovering relationships between variables as it analyzes data points.
 
-In this series of lessons, you'll discover the difference between Linear vs. Logistic Regression, and when you should use one or the other.
+In this series of lessons, you'll discover the difference between linear vs. logistic regression, and when you should use one or the other.
 
 In this group of lessons, you will get set up to begin machine learning tasks, including configuring Visual Studio code to manage notebooks, the common environment for data scientists. You will discover Scikit-learn, a library for machine learning, and you will build your first models, focusing on Regression models in this chapter.
 
-> There are useful low-code tools that can help you learn about working with Regression models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+> There are useful low-code tools that can help you learn about working with regression models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
 
 ### Lessons
 
-1. [Tools of the Trade](1-Tools/README.md)
-2. [Managing Data](2-Data/README.md)
-3. [Linear and Polynomial Regression](3-Linear/README.md)
-4. [Logistic Regression](4-Logistic/README.md)
+1. [Tools of the trade](1-Tools/README.md)
+2. [Managing data](2-Data/README.md)
+3. [Linear and polynomial regression](3-Linear/README.md)
+4. [Logistic regression](4-Logistic/README.md)
 
 ---
 ### Credits
 
-"ML with Regression" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper)
+"ML with regression" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper)
 
 ♥️ Quiz contributors include: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) and [Ornella Altunyan](https://twitter.com/ornelladotcom)
 

4-Classification/README.md

@@ -1,4 +1,4 @@
-# Getting Started with Classification
+# Getting started with classification
 ## Regional topic: Delicious Asian and Indian Cuisines 🍜
 
 In Asia and India, food traditions are extremely diverse, and very delicious! Let's look at data about regional cuisines to try to guess where they originated.
@@ -8,18 +8,18 @@ In Asia and India, food traditions are extremely diverse, and very delicious! Le
   
 ## What you will learn
 
-In this section, you will build on the skills you learned in Lesson 1 (Regression) to learn about other classifiers you can use that will help you learn about your data.
+In this section, you will build on the skills you learned in the first part of this curriculum all about regressionn to learn about other classifiers you can use that will help you learn about your data.
 
-> There are useful low-code tools that can help you learn about working with Classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+> There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
 
 ## Lessons
 
-1. [Introduction to Classification](1-Introduction/README.md)
-2. [More Classifiers](2-Classifiers-1/README.md)
-3. [Yet Other Classifiers](3-Classifiers-2/README.md)
-4. [Applied ML: Build a Web App](4-Applied/README.md)
+1. [Introduction to classification](1-Introduction/README.md)
+2. [More classifiers](2-Classifiers-1/README.md)
+3. [Yet other classifiers](3-Classifiers-2/README.md)
+4. [Applied ML: build a web app](4-Applied/README.md)
 ## Credits
 
-"Getting Started with Classification" was written with ♥️ by [Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper)
+"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 cuisines dataset was sourced from [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)

5-Clustering/README.md

@@ -1,5 +1,5 @@
-# Clustering Models for Machine Learning
-## Regional topic: Clustering models for a Nigerian audience's musical taste 🎧
+# Clustering models for machine learning
+## 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!
 
@@ -8,17 +8,17 @@ Nigeria's diverse audience has diverse musical tastes. Using data scraped from S
 Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
 
-In this series of lessons, you will discover new ways to analyze data using Clustering techniques. Clustering is particularly useful when your dataset lacks labels. If it does have labels, then Classification techniques such as those you learned in previous lessons are more useful. But in cases where you are looking to group unlabelled data, clustering is a great way to discover patterns.
+In this series of lessons, you will discover new ways to analyze data using clustering techniques. Clustering is particularly useful when your dataset lacks labels. If it does have labels, then classification techniques such as those you learned in previous lessons might be more useful. But in cases where you are looking to group unlabelled data, clustering is a great way to discover patterns.
 
-> There are useful low-code tools that can help you learn about working with Clustering models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+> There are useful low-code tools that can help you learn about working with clustering models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
 ## Lessons
 
-1. [Introduction to Clustering](1-Visualize/README.md)
-2. [K-Means Clustering](2-K-Means/README.md)
+1. [Introduction to clustering](1-Visualize/README.md)
+2. [K-Means clustering](2-K-Means/README.md)
 ## Credits
 
 These lessons were written with 🎶 by [Jen Looper](https://www.twitter.com/jenlooper) with helpful reviews by [Rishit Dagli](https://rishit_dagli) and [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan).
 
 The [Nigerian Songs](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) dataset was sourced from Kaggle as scraped from Spotify.
 
-Useful K-Means examples that aided in creating this lesson include this [iris exploration](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), this [introductory notebook](https://www.kaggle.com/prashant111/k-means-clustering-with-python), this [hypothetical NGO example](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering) and 
+Useful K-Means examples that aided in creating this lesson include this [iris exploration](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), this [introductory notebook](https://www.kaggle.com/prashant111/k-means-clustering-with-python), and this [hypothetical NGO example](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering).

6-NLP/README.md

@@ -1,22 +1,21 @@
-# Getting Started with natural language processing 
+# Getting started with natural language processing 
 
-## Regional topic: European literature and Romantic Hotels of Europe ❤️ 
+## Regional topic: European languages and 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 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.
+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.
 
 ![Pride and Prejudice book and tea](images/p&p.jpg)
 > Photo by <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> on <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Lessons
 
 1. [Introduction to natural language processing](1-Introduction-to-NLP/README.md)
-2. [Common NLP Tasks and Techniques](2-Tasks/README.md)
-3. [Translation and Sentiment Analysis with Machine Learning](3-Translation-Sentiment/README.md)
+2. [Common NLP tasks and techniques](2-Tasks/README.md)
+3. [Translation and sentiment analysis with machine learning](3-Translation-Sentiment/README.md)
 4. TBD
-5. TBD
 
 ## Credits 
 
-These natural language processing lessons were written with ☕ by [Stephen Howell]([Twitter](https://twitter.com/Howell_MSFT))
+These natural language processing lessons were written with ☕ by [Stephen Howell](https://twitter.com/Howell_MSFT)

7-TimeSeries/README.md

@@ -1,21 +1,18 @@
-# Time Series Forecasting
+# Introduction to time series forecasting
+## Regional topic: worldwide electricity usage ✨
 
-## 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.
+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.
 
 ![electric grid](images/electric-grid.jpg)
 
 Photo by <a href="https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Peddi Sai hrithik</a> of electrical towers on a road in Rajasthan on <a href="https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
-
-
 ## Lessons
 
-1. [Introduction to Time Series Forecasting](1-Introduction/README.md)
-2. [Building ARIMA Time Series Models](2-ARIMA/README.md)
+1. [Introduction to time series forecasting](1-Introduction/README.md)
+2. [Building ARIMA time series models](2-ARIMA/README.md)
 
 ## Credits
 
-"Time Series Forecasting" was written with ⚡️ by [Francesca Lazzeri](https://twitter.com/frlazzeri) and [Jen Looper](https://twitter.com/jenlooper)
+"Introduction to time series forecasting" was written with ⚡️ by [Francesca Lazzeri](https://twitter.com/frlazzeri) and [Jen Looper](https://twitter.com/jenlooper)