incorporating Rishit's review

Regression/1-Tools/README.md

@@ -64,9 +64,11 @@ According to their [website](https://scikit-learn.org/stable/getting_started.htm
 
 > 🎓 **[Model Fitting](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py)** in the context of machine learning refers to the accuracy of the model's underlying function as it attempts to analyze data with which it is not familiar. **Underfitting** and **overfitting** are common problems that degrade the quality of the model as the model fits either not well enough or too well. This causes the model to make predictions either too closely aligned or too loosely aligned with its training data. An overfit model predicts training data too well because it has learned the data's details and noise too well. An underfit model is not accurate as it can neither accurately analyze its training data nor data it has not yet 'seen'.
 
+TODO: Infographic to show underfitting/overfitting like this https://miro.medium.com/max/1525/1*TzMis7bbuaU1OE2q64hnbg.png
+
 > 🎓 **Data Preprocessing** is the process whereby data scientists clean and convert data for use in the machine learning lifecycle. 
 
-> 🎓 **Model Selection and Evaluation** is the process whereby data scientists evaluate the accuracy of a model by feeding it unseen data, selecting the most appropriate model for the task at hand.
+> 🎓 **Model Selection and Evaluation** is the process whereby data scientists evaluate the accuracy of a model or any other relevant metric of a model by feeding it unseen data, selecting the most appropriate model for the task at hand.
 
 In this course, you will use Scikit-Learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum. 
 
@@ -100,6 +102,10 @@ s1 tc: T-Cells (a type of white blood cells)
 
 ✅ This dataset includes the concept of 'sex' as a feature variable important to research around diabetes. Many medical datasets include this type of binary classification. Think a bit about how categorizations such as this might exclude certain parts of a population from treatments.
 
+Now, load up the X and y data.
+
+> 🎓 Remember, this is supervised learning, and we need a named 'y' target.
+
 3. In a new cell, load the diabetes dataset as data and target (X and y, loaded as a tuple). X will be a data matrix, and y will be the regression target. Add some print commands to show the shape of the data matrix and its first element:
 
 > 🎓 A **tuple** is an [ordered list of elements](https://en.wikipedia.org/wiki/Tuple).

Regression/2-Data/README.md

@@ -87,12 +87,12 @@ pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=
 If you print the data now, you can see that you are only getting the 415 or so rows of data containing pumpkins by the bushel. But wait! there's one more thing to do. Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, so do some math to standardize it. Add these lines after the block creating the new_pumpkins dataframe:
 
 ```python
-new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1
+new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
 
-new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2
+new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
 ```
 
-✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. "A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds." It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel.
+✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. "A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds." It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel. All this study of bushels of pumpkins, however, goes to show how very important it is to understand the nature of your data!
 
 Now, you can analyze the pricing per unit based on their bushel measurement. If you print out the data one more time, you can see how it's standardized.
 

Regression/3-Linear/README.md

@@ -26,13 +26,13 @@ As you learned in Lesson 1, the goal of a linear regression exercise is to be ab
 > 
 > This line has an equation: `Y = a + bX`. It is typical of **Least-Squares Regression** to draw this type of line. 
 >
-> `X` is the 'explanatory variable'. `Y` is the 'dependent variable'. The slope of the line is `b` and `a` is the intercept, which refers to the value of `Y` when `X = 0`. 
+> `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`. 
 >
 > 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. 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`.
 >
 > You can observe the method of calculation for these values on the [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) web site.
 >
-> A common method of regression is **Least-Squares Regression** which means that all the datapoints surounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`.
+> A common method of regression is **Least-Squares Regression** which means that all the datapoints surounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`. We do so since we want to model a line that has the least cumulative distance from all of our data points. We also square the terms before adding them since we are concerned with its magnitude rather than its direction.
 >
 > One more term to understand is the **Correlation Coefficient** between given X and Y variables. For a scatterplot, you can quickly visualize this coefficient. A plot with datapoints scattered in a neat line have high correlation, but a plot with datapoints scattered everywhere between X and Y have a low correlation.
 >
@@ -253,6 +253,6 @@ Test several different variables in this notebook to see how correlation corresp
 
 ## Review & Self Study
 
-In this lesson we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques.
+In this lesson we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
 
 **Assignment**: [Build a Model](assignment.md)