Fixed some grammatical errors (microsoft#395)

* Fixed some grammatical errors

* Fixed some grammatical errors

* Update README.md

Author: iacs-csu-2020

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

@@ -7,13 +7,13 @@
 
 In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence.
 
-The history of artificial intelligence, AI, as a field is intertwined with the history of machine learning, as the algorithms and computational advances that underpin ML fed into the development of AI. It is useful to remember that, while these fields as distinct areas of inquiry began to crystallize in the 1950s, important [algorithmical, statistical, mathematical, computational and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning) predated and overlapped this era. In fact, people have been thinking about these questions for [hundreds of years](https://wikipedia.org/wiki/History_of_artificial_intelligence): this article discusses the historical intellectual underpinnings of the idea of a 'thinking machine.'
+The history of artificial intelligence (AI) as a field is intertwined with the history of machine learning, as the algorithms and computational advances that underpin ML fed into the development of AI. It is useful to remember that, while these fields as distinct areas of inquiry began to crystallize in the 1950s, important [algorithmic, statistical, mathematical, computational and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning) predated and overlapped this era. In fact, people have been thinking about these questions for [hundreds of years](https://wikipedia.org/wiki/History_of_artificial_intelligence): this article discusses the historical intellectual underpinnings of the idea of a 'thinking machine.'
 
 ## Notable discoveries
 
 - 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) and its predecessors. This theorem and its applications underlie inference, describing the probability of an event occurring based on prior knowledge.
 - 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) by French mathematician Adrien-Marie Legendre. This theory, which you will learn about  in our Regression unit, helps in data fitting.
-- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) named after Russian mathematician Andrey Markov is used to describe a sequence of possible events based on a previous state.
+- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain), named after Russian mathematician Andrey Markov, is used to describe a sequence of possible events based on a previous state.
 - 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) is a type of linear classifier invented by American psychologist Frank Rosenblatt that underlies advances in deep learning.
 - 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) is an algorithm originally designed to map routes. In an ML context it is used to  detect patterns.
 - 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) is used to train [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network).
@@ -89,7 +89,7 @@ This epoch saw a new era for ML and AI to be able to solve some of the problems
 
 ## Now
 
-Today, machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+Today machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
 
 It remains to be seen what the future holds, but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
 

2-Regression/README.md

@@ -13,7 +13,7 @@ In North America, pumpkins are often carved into scary faces for Halloween. Let'
 
 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 differences between linear and logistic regression, and when you should prefer one over 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.