Merge pull request #665 from jasleen101010/main

Fixed hyperlinks and knitted the Rmd files to html for beginning half of the repo
microsoft · Jul 3, 2023 · 1cad50d · 1cad50d
2 parents bf78cf8 + 8084e84
commit 1cad50d
Show file tree

Hide file tree

Showing 16 changed files with 19,690 additions and 1,558 deletions.
diff --git a/.gitignore b/.gitignore
@@ -355,3 +355,9 @@ MigrationBackup/
 
 # Mac-specific
 .DS_Store
+.Rproj.user
+
+.Rdata
+.Rhistory
+ML-For-Beginners.Rproj
+
diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
@@ -6,7 +6,7 @@
 
 ## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/)
 
-> ### [This lesson is available in R!](./solution/R/lesson_1-R.ipynb)
+> ### [This lesson is available in R!](./solution/R/lesson_1.html)
 
 ## Introduction
 

diff --git a/2-Regression/1-Tools/solution/R/lesson_1.html b/2-Regression/1-Tools/solution/R/lesson_1.html
diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
@@ -6,7 +6,7 @@ Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11/)
 
-> ### [This lesson is available in R!](./solution/R/lesson_2-R.ipynb)
+> ### [This lesson is available in R!](./solution/R/lesson_2.html)
 
 ## Introduction
 

diff --git a/2-Regression/2-Data/solution/R/lesson_2.html b/2-Regression/2-Data/solution/R/lesson_2.html
diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md
@@ -4,7 +4,7 @@
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 ## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/)
 
-> ### [This lesson is available in R!](./solution/R/lesson_3-R.ipynb)
+> ### [This lesson is available in R!](./solution/R/lesson_3.html)
 ### Introduction 
 
 So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using Matplotlib.

diff --git a/2-Regression/3-Linear/solution/R/lesson_3.Rmd b/2-Regression/3-Linear/solution/R/lesson_3.Rmd
@@ -84,8 +84,8 @@ We do so since we want to model a line that has the least cumulative distance fr
 >
 > 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.
 >
-> ![Infographic by Jen Looper](../../images/calculation.png)
->
+![Infographic by Jen Looper](../../images/calculation.png)
+
 > Calculate the value of Y. If you're paying around \$4, it must be April!
 >
 > 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`.
@@ -114,7 +114,7 @@ Load up required libraries and dataset. Convert the data to a data frame contain
 
 -   Convert the price to reflect the pricing by bushel quantity
 
-> We covered these steps in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb).
+> We covered these steps in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2.html).
 
 ```{r load_tidy_verse_models, message=F, warning=F}
 # Load the core Tidyverse packages
@@ -285,7 +285,7 @@ That's an awesome thought! You see, once your recipe is defined, you can estimat
 
 For that, you'll need two more verbs: `prep()` and `bake()` and as always, our little R friends by [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) help you in understanding this better!
 
-![Artwork by \@allison_horst](../images/recipes.png){width="550"}
+![Artwork by \@allison_horst](../../images/recipes.png){width="550"}
 
 [`prep()`](https://recipes.tidymodels.org/reference/prep.html): estimates the required parameters from a training set that can be later applied to other data sets. For instance, for a given predictor column, what observation will be assigned integer 0 or 1 or 2 etc
 

diff --git a/2-Regression/3-Linear/solution/R/lesson_3.html b/2-Regression/3-Linear/solution/R/lesson_3.html
diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
@@ -4,7 +4,7 @@
 
 ## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)
 
-> ### [This lesson is available in R!](./solution/R/lesson_4-R.ipynb)
+> ### [This lesson is available in R!](./solution/R/lesson_4.html)
 
 ## Introduction
 

diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
@@ -14,7 +14,7 @@ output:
 
 ![Infographic by Dasani Madipalli](../../images/logistic-linear.png){width="600"}
 
-#### ** [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**
+#### **[Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**
 
 ####  Introduction
 
@@ -56,7 +56,7 @@ suppressWarnings(if (!require("pacman"))install.packages("pacman"))
 pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)
 ```
 
-## ** Define the question**
+## **Define the question**
 
 For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.
 
@@ -148,7 +148,7 @@ pumpkins_select %>%
 
 The goal of data exploration is to try to understand the `relationships` between its attributes; in particular, any apparent correlation between the *features* and the *label* your model will try to predict. One way of doing this is by using data visualization.
 
-Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3-R.ipynb).
+Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3.html).
 
 Tidymodels provides yet another neat package: [recipes](https://recipes.tidymodels.org/)- a package for preprocessing data. We'll define a `recipe` that specifies that all predictor columns should be encoded into a set of integers , `prep` it to estimates the required quantities and statistics needed by any operations and finally `bake` to apply the computations to new data.
 

diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.html b/2-Regression/4-Logistic/solution/R/lesson_4.html
diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
@@ -21,7 +21,7 @@ Classification uses various algorithms to determine other ways of determining a
 
 ## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)
 
-> ### [This lesson is available in R!](./solution/R/lesson_10-R.ipynb)
+> ### [This lesson is available in R!](./solution/R/lesson_10.html)
 
 ### Introduction
 

diff --git a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd
@@ -393,7 +393,7 @@ Let's now save a copy of this data for use in future lessons:
 
 ```{r save_preproc_data}
 # Save preprocessed data
-write_csv(preprocessed_df, "../../data/cleaned_cuisines_R.csv")
+write_csv(preprocessed_df, "../../../data/cleaned_cuisines_R.csv")
 
 ```
 

diff --git a/4-Classification/1-Introduction/solution/R/lesson_10.html b/4-Classification/1-Introduction/solution/R/lesson_10.html