spelling audits

1-Introduction/1-intro-to-ML/README.md

@@ -47,7 +47,7 @@ Although the terms can be confused, machine learning (ML) is an important subset
 
 In this curriculum, we are going to cover only the core concepts of machine learning that a beginner must know. We cover what we call 'Classical machine learning' primarily using Scikit-learn, an excellent library many students use to learn the basics.  To understand broader concepts of artificial intelligence or deep learning, a strong fundamental knowledge of machine learning is indispensable, and so we would like to offer it here. 
 
-You will additionally learn the basics of Regression, Classification, Clustering, Natural Language Processing, Time Series Forecasting, and Reinforcement Learning, as well as real-world applications, the history of ML, ML and Fairness, and how to use your model in web apps.
+You will additionally learn the basics of Regression, Classification, Clustering, natural language processing, Time Series Forecasting, and Reinforcement Learning, as well as real-world applications, the history of ML, ML and Fairness, and how to use your model in web apps.
 
 In this course you will learn:
 
@@ -58,7 +58,7 @@ In this course you will learn:
 - Regression
 - Classification
 - Clustering
-- Natural Language Processing
+- natural language processing
 - Time Series Forecasting
 - Reinforcement Learning
 - Real-world applications

1-Introduction/2-history-of-ML/README.md

@@ -38,7 +38,7 @@ The workshop is credited with having initiated and encouraged several discussion
 
 From the 1950s through the mid '70s, optimism ran high in the hope that AI could solve many problems. In 1967, Marvin Minsky stated confidently that "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
 
-Natural Language Processing research flourished, search was refined and made more powerful, and the concept of 'micro-worlds' was created, where simple tasks were completed using plain language instructions.
+natural language processing research flourished, search was refined and made more powerful, and the concept of 'micro-worlds' was created, where simple tasks were completed using plain language instructions.
 
 Research was well funded by government agencies, advances were made in computation and algorithms, and prototypes of intelligent machines were built. Some of these machines include:
 

6-NLP/1-Introduction-to-NLP/README.md

@@ -1,6 +1,6 @@
-# Introduction to Natural Language Processing
+# Introduction to natural language processing
 
-This lesson covers a brief history and important concepts of *Computational Linguistics* focusing on *Natural Language Processing*.
+This lesson covers a brief history and important concepts of *computational linguistics* focusing on *natural language processing*.
 
 [![NLP 101](https://img.youtube.com/vi/C75SiVhXjRM/0.jpg)](https://youtu.be/C75SiVhXjRM "NLP 101")
 
@@ -13,7 +13,7 @@ NLP, as it is commonly known, is one of the best-known areas where machine learn
 
 You will learn about how the ideas about languages developed and what the major areas of study have been. You will also learn definitions and concepts about how computers process text, including parsing, grammar, and identifying nouns and verbs. There are some coding tasks in this lesson, and several important concepts are introduced that you will learn to code later on in the next lessons. 
 
-Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. Natural Language Processing (NLP) is a related field focused on how computers can process 'natural', or human, languages. If you have ever dictated to your phone instead of typing or asked a virtual assistant a question, your speech was converted into a text form and then processed or *parsed* from the language you spoke. The detected keywords were then processed into a format that the phone or assistant could understand and act on.
+Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. natural language processing (NLP) is a related field focused on how computers can process 'natural', or human, languages. If you have ever dictated to your phone instead of typing or asked a virtual assistant a question, your speech was converted into a text form and then processed or *parsed* from the language you spoke. The detected keywords were then processed into a format that the phone or assistant could understand and act on.
 
 This is possible because someone wrote a computer program to do this. A few decades ago, some science fiction writers predicted that people would mostly speak to their computers, and the computers would always understand exactly what they meant. Sadly, it turned out to be a harder problem that many imagined, and while it is a much better understood problem today, there are significant challenges in achieving 'perfect' natural language processing when it comes to understanding the meaning of a sentence. This is a particularly hard problem when it comes to understanding humour or detecting emotions such as sarcasm in a sentence.
 

6-NLP/2-Tasks/README.md

@@ -1,6 +1,6 @@
-# Common Natural Language Processing Tasks and Techniques
+# Common natural language processing Tasks and Techniques
 
-For most *Natural Language Processing* tasks, the text to be processed must be broken down, examined, and the results stored or cross referenced with rules and data sets. This allows the programmer to derive the meaning or intent or only the frequency of terms and words in a text.
+For most *natural language processing* tasks, the text to be processed must be broken down, examined, and the results stored or cross referenced with rules and data sets. This allows the programmer to derive the meaning or intent or only the frequency of terms and words in a text.
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/33/)
 
 

6-NLP/README.md

@@ -1,8 +1,8 @@
-# Getting Started with Natural Language Processing 
+# 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 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.
 
@@ -11,12 +11,12 @@ In these lessons we'll learn the basics of NLP by building small conversational
   
 ## Lessons
 
-1. [Introduction to Natural Language Processing](1-Introduction-to-NLP/README.md)
+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)
 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]([Twitter](https://twitter.com/Howell_MSFT))

9-Real-World/1-Applications/README.md

@@ -1,6 +1,6 @@
 # Machine Learning in the Real World
 
-In this curriculum, you have learned many ways to prepare data for training and create machine learning models. You built a series of classic Regression, Clustering, Classification, Natural Language Processing, and Time Series models. Congratulations! Now, you might be wondering what it's all for... what are the real world applications for these models?
+In this curriculum, you have learned many ways to prepare data for training and create machine learning models. You built a series of classic Regression, Clustering, Classification, natural language processing, and Time Series models. Congratulations! Now, you might be wondering what it's all for... what are the real world applications for these models?
 
 While a lot of interest in industry has been garnered by AI, which usually leverages deep learning, there are still valuable applications for classical machine learning models. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users.