removing en. references

Clustering/1-Visualize/README.md

@@ -55,7 +55,7 @@ Deepen your understanding of Clustering techniques in this [Learn module](https:
 > 
 > Derived from mathematical terminology, non-flat vs. flat geometry refers to the measure of distances between points by either 'flat' ([Euclidean](https://wikipedia.org/wiki/Euclidean_geometry)) or 'non-flat' (non-Euclidean) geometrical methods. 
 >
->'Flat' in this context refers to Euclidean geometry (parts of which are taught as 'plane' geometry), and non-flat refers to non-Euclidean geometry. What does geometry have to do with machine learning? Well, as two fields that are rooted in mathematics, there must be a common way to measure distances between points in clusters, and that can be done in a 'flat' or 'non-flat' way, depending on the nature of the data. [Euclidean distances](https://en.wikipedia.org/wiki/Euclidean_distance) are measured as the length of a line segment between two points. [Non-Euclidean distances](https://en.wikipedia.org/wiki/Non-Euclidean_geometry) are measured along a curve. If your data, visualized, seems to not exist on a plane, you might need to use a specialized algorithm to handle it.
+>'Flat' in this context refers to Euclidean geometry (parts of which are taught as 'plane' geometry), and non-flat refers to non-Euclidean geometry. What does geometry have to do with machine learning? Well, as two fields that are rooted in mathematics, there must be a common way to measure distances between points in clusters, and that can be done in a 'flat' or 'non-flat' way, depending on the nature of the data. [Euclidean distances](https://wikipedia.org/wiki/Euclidean_distance) are measured as the length of a line segment between two points. [Non-Euclidean distances](https://wikipedia.org/wiki/Non-Euclidean_geometry) are measured along a curve. If your data, visualized, seems to not exist on a plane, you might need to use a specialized algorithm to handle it.
 >
 > Infographic: like the last one here https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering
 > 
@@ -85,7 +85,7 @@ TODO: infographic
 
 **Centroid clustering** 
 
-This popular algorithm requires the choice of 'k', or the number of clusters to form, after which the algorithm determines the center point of a cluster and gathers data around that point. [K-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) is a popular version of centroid clustering. The center is determined by the nearest mean, thus the name. The squared distance from the cluster is minimized.
+This popular algorithm requires the choice of 'k', or the number of clusters to form, after which the algorithm determines the center point of a cluster and gathers data around that point. [K-means clustering](https://wikipedia.org/wiki/K-means_clustering) is a popular version of centroid clustering. The center is determined by the nearest mean, thus the name. The squared distance from the cluster is minimized.
 
 TODO: infographic
 

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

@@ -22,13 +22,15 @@ The history of Artificial Intelligence as a field is intertwined with the histor
 
 ## 1950: Machines that Think
 
-Alan Turing, a truly remarkable person who was voted [by the public in 2019](https://en.wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) as the greatest scientist of the 20th century, is credited as helping to lay the foundation for the concept of a 'machine that can think'.
+Alan Turing, a truly remarkable person who was voted [by the public in 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) as the greatest scientist of the 20th century, is credited as helping to lay the foundation for the concept of a 'machine that can think'. He grappled with naysayers and his own need for empirical evidence of this concept in part by creating the [Turing Test](https://www.bbc.com/news/technology-18475646), which you will explore in our NLP lessons.
 
-## 1956: Dartmouth Research Project
+## 1956: Dartmouth Summer Research Project
 
-"To proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it -- John McCarthy" 
+"The Dartmouth Summer Research Project on Artificial Intelligence was a seminal event for artificial intelligence as a field"[source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth) and it was here that the term 'Artificial Intelligence' was coined.
 
-They named the field of "Artificial Intelligence". This is the first time the phrase was coined.
+The lead researcher, mathematics professor John McCarthy, hoped "to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it". The participants included another luminary in the field, Marvin Minsky.
+
+The Workshop is credited with having initiated and encouraged including "the rise of symbolic methods, systems focussed on limited domains (early expert systems), and deductive systems versus inductive systems."[source](https://wikipedia.org/wiki/Dartmouth_workshop)
 
 ## 1956 - 1974: "The Gold Rush"
 

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

@@ -49,9 +49,9 @@ The history of trying to make computers understand human language goes back deca
 
 The idea for this came from a party game called *The Imitation Game* where an interrogator is alone in a room and tasked with determining which of two people (in another room) are male and female respectively. The interrogator can send notes, and must try to think of questions where the written answers reveal the gender of the mystery person. Of course, the players in the other room are trying to trick the interrogator by answering questions in such as way as to mislead or confuse the interrogator, whilst also giving the appearance of answering honestly.
 
-In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https://en.wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". This gave the impression that Eliza understood the statement and was asking a follow-on question, whereas in reality, it was changing the tense and adding some words. If Eliza could not identify a keyword that it had a response for, it would instead give a random response that could be applicable to many different statements. Eliza could be easily tricked, for instance if a user wrote "**You are** a <u>bicycle</u>" it might respond with "How long have **I been** a <u>bicycle</u>?", instead of a more reasoned response. 
+In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https:/wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". This gave the impression that Eliza understood the statement and was asking a follow-on question, whereas in reality, it was changing the tense and adding some words. If Eliza could not identify a keyword that it had a response for, it would instead give a random response that could be applicable to many different statements. Eliza could be easily tricked, for instance if a user wrote "**You are** a <u>bicycle</u>" it might respond with "How long have **I been** a <u>bicycle</u>?", instead of a more reasoned response. 
 
-> Note: You can read the original description of [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) published in 1966 if you have an ACM account. Alternately, read about Eliza on [wikipedia](https://en.wikipedia.org/wiki/ELIZA)
+> Note: You can read the original description of [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) published in 1966 if you have an ACM account. Alternately, read about Eliza on [wikipedia](https://wikipedia.org/wiki/ELIZA)
 
 ### Task: Coding a basic conversational bot
 

NLP/2-Tasks/README.md

@@ -14,7 +14,7 @@ Let's discover common techniques used in processing text. Combined with machine
 
 🎓 **Embeddings**
 
-> [Word embeddings](https://en.wikipedia.org/wiki/Word_embedding) are a way to convert your text data numerically. This is done in a way so that words with a similar meaning or words used together cluster together.
+> [Word embeddings](https://wikipedia.org/wiki/Word_embedding) are a way to convert your text data numerically. This is done in a way so that words with a similar meaning or words used together cluster together.
 
 ✅ Try [this interesting tool](https://projector.tensorflow.org/) to experiment with word embeddings. Clicking on one word shows clusters of similar words: 'toy' clusters with 'disney', 'lego', 'playstation', and 'console'.
 

Regression/4-Logistic/README.md

@@ -171,7 +171,7 @@ Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
 
 While you can get a scoreboard report [terms](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) by printing out the items above, you might be able to understand your model more easily by using  a [confusion matrix](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix) to help us understand how the model is performing.
 
-> 🎓 A '[confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix)' (or 'error matrix') is a table that expresses your model's true vs. false positives and negatives, thus gauging the accuracy of predictions.
+> 🎓 A '[confusion matrix](https://wikipedia.org/wiki/Confusion_matrix)' (or 'error matrix') is a table that expresses your model's true vs. false positives and negatives, thus gauging the accuracy of predictions.
 
 ```python
 from sklearn.metrics import confusion_matrix