infographics and credits

2-Regression/3-Linear/README.md

@@ -50,12 +50,12 @@ We do so since we want to model a line that has the least cumulative distance fr
 > `X` is the 'explanatory variable'. `Y` is the 'dependent variable'. The slope of the line is `b` and `a` is the y-intercept, which refers to the value of `Y` when `X = 0`. 
 >
 >![calculate the slope](images/slope.png)
-> First, calculate the slope `b`
+> First, calculate the slope `b`. Infographic by [Jen Looper](https://twitter.com/jenlooper)
 >
 > In other words, and referring to our pumpkin data's original question: "predict the price of a pumpkin per bushel by month", `X` would refer to the price and `Y` would refer to the month of sale. 
 >
 >![complete the equation](images/calculation.png)
-> Calculate the value of Y. If you're paying around $4, it must be April!
+> Calculate the value of Y. If you're paying around $4, it must be April! Infographic by [Jen Looper](https://twitter.com/jenlooper)
 >
 > The math that calculates the line must demonstrate the slope of the line, which is also dependent on the intercept, or where `Y` is situated when `X = 0`.
 >

4-Classification/1-Introduction/README.md

@@ -2,20 +2,26 @@
 
 In these four lessons, you will discover the 'meat and potatoes' of classic machine learning - classification. No pun intended - we will walk through using various classification algorithms with a dataset all about the brilliant cuisines of Asia and India. Hope you're hungry!
 
-Classification is a form of [supervised learning](https://wikipedia.org/wiki/Supervised_learning) that bears a lot in common with regression techniques. If machine learning is all about assigning names to things via datasets, then classification generally falls into two groups: binary classification and multiclass classification.
+Classification is a form of [supervised learning](https://wikipedia.org/wiki/Supervised_learning) that bears a lot in common with regression techniques. If machine learning is all about predicting values or names to things by using datasets, then classification generally falls into two groups: binary classification and multiclass 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
 
-Remember, linear regression helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict what price a pumpkin would be in September vs. December, for example. Logistic Regression helped you discover binary categories: at this price point, is this pumpkin orange or not-orange?
+Remember, linear regression helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict what price a pumpkin would be in September vs. December, for example. Logistic regression helped you discover binary categories: at this price point, is this pumpkin orange or not-orange?
 
 Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
 
 ### Introduction
 
-Classification is one of the fundamental activities of the machine learning researcher and data scientist. From basic classification of a binary value ("is this email spam or not?") to complex image classification and segmentation using computer vision, it's always useful to be able to sort data into classes and ask questions of it. Or, to state the process in a more scientific way, your classification method creates a predictive model that enables you to map the relationship between input variables to output variables. 
+Classification is one of the fundamental activities of the machine learning researcher and data scientist. From basic classification of a binary value ("is this email spam or not?") to complex image classification and segmentation using computer vision, it's always useful to be able to sort data into classes and ask questions of it. 
+
+To state the process in a more scientific way, your classification method creates a predictive model that enables you to map the relationship between input variables to output variables. 
+
+![binary vs. multiclass classification](images/binary-multiclass.png)
+
+> Binary vs. multiclass problems for classification algorithms to handle. Infographic by [Jen Looper](https://twitter.com/jenlooper)
 
 Before starting the process of cleaning our data, visualizing it, and prepping it for our ML tasks, let's learn a bit about the various ways machine learning can be leveraged to classify data.