fix(docs): corrects common typos in quiz and project markdown files

2_R_Programming/projects/project1.md

@@ -197,7 +197,7 @@ corr <- function(directory, threshold = 0){
     count = nrow(completeCases)
 
     ## Calculate and store the count of complete cases
-    ## if threshhold is reached
+    ## if threshold is reached
     if( count >= threshold ) {
       correlations = c(correlations, cor(completeCases$nitrate, completeCases$sulfate) )
     }

3_Getting_and_Cleaning_Data/quizzes/quiz4.md

@@ -33,7 +33,7 @@ Remove the commas from the GDP numbers in millions of dollars and average them.
 Original data sources: http://data.worldbank.org/data-catalog/GDP-ranking-table
 ```R
 # Removed the s from https to be compatible with windows computers. 
-# Skip first 5 rows and only read in relevent columns
+# Skip first 5 rows and only read in relevant columns
 GDPrank <- data.table::fread('http://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FGDP.csv'
                     , skip=5
                     , nrows=190

7_Regression_Models/quizzes/quiz4.md

@@ -33,7 +33,7 @@ Remove the commas from the GDP numbers in millions of dollars and average them.
 Original data sources: http://data.worldbank.org/data-catalog/GDP-ranking-table
 ```R
 # Removed the s from https to be compatible with windows computers. 
-# Skip first 5 rows and only read in relevent columns
+# Skip first 5 rows and only read in relevant columns
 GDPrank <- data.table::fread('http://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FGDP.csv'
                     , skip=5
                     , nrows=190

9_Developing_Data_Products/quizzes/quiz4.md

@@ -33,7 +33,7 @@ Remove the commas from the GDP numbers in millions of dollars and average them.
 Original data sources: http://data.worldbank.org/data-catalog/GDP-ranking-table
 ```R
 # Removed the s from https to be compatible with windows computers. 
-# Skip first 5 rows and only read in relevent columns
+# Skip first 5 rows and only read in relevant columns
 GDPrank <- data.table::fread('http://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FGDP.csv'
                     , skip=5
                     , nrows=190

Stanford_Machine_Learning/Week3/week3quiz2.md

@@ -28,7 +28,7 @@ True or False | Statement | Explanation
 False | Using a very large value λ cannot hurt the performance of your hypothesis; the only reason we do not set to be too large is to avoid numerical problems. | Using a very large value of λ can lead to underfitting of the training set.
 False | Because regularization causes J(θ) to no longer be convex, gradient descent may not always converge to the global minimum (when λ > 0, and when using an appropriate learning rate α). | Regularized logistic regression and regularized linear regression are both convex, and thus gradient descent will still converge to the global minimum.
 True | Using too large a value of λ can cause your hypothesis to underfit the data.| A large value of results in a large λ regularization penalty and thus a strong preference for simpler models which can underfit the data.
-False | Because logistic regression outputs values 0 <= h<sub>0</sub> <= 1, its range of output values can only be "shrunk" slighly by regularization anyway, so regularization is generally not helpful for it.  | None needed
+False | Because logistic regression outputs values 0 <= h<sub>0</sub> <= 1, its range of output values can only be "shrunk" slightly by regularization anyway, so regularization is generally not helpful for it.  | None needed
 
 Question 4
 ----------

Stanford_Machine_Learning/Week8/UnsupervisedLearningQuiz.md

@@ -49,7 +49,7 @@ True or False | Statement | Explanation
 --- | --- | ---
 False | Once an example has been assigned to a particular centroid, it will never be reassigned to another centroid | Not sure yet
 True | A good way to initialize K-means is to select K (distinct) examples from the training set and set the cluster centroids equal to these selected examples. | This is the recommended method of initialization.
-True | On every iteration of K-means, the cost funtion J(c<sup>(1)</sup>, ..., c<sup>(m)</sup>,  μ<sub>1</sub>, ...,  μ<sub>k</sub> (the distortion function) should either stay the same or decrease; in particular, it should not increase | True  
+True | On every iteration of K-means, the cost function J(c<sup>(1)</sup>, ..., c<sup>(m)</sup>,  μ<sub>1</sub>, ...,  μ<sub>k</sub> (the distortion function) should either stay the same or decrease; in particular, it should not increase | True  
 False | K-Means will always give the same results regardless of the initialization of the centroids. | K-means is sensitive to different initializations, which is why you should run it multiple times from different random initializations
 True | For some datasets, the "right" or "correct" value of K (the number of clusters) can be ambiguous, and hard even for a human expert looking carefully at the data to decide. | Look at an elbow curve for an example. It can often be ambiguous.
 True | If we are worried about K-means getting stuck in bad local optima, one way to ameliorate (reduce) this problem is if we try using multiple random initializations. | None needed