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diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
@@ -6,7 +6,7 @@
 
 ## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
 
-> ### [This lesson is available in R!](./solution/lesson_1-R.ipynb)
+> ### [This lesson is available in R!](./solution/R/lesson_1-R.ipynb)
 
 ## Introduction
 

diff --git a/2-Regression/1-Tools/solution/Julia/README.md b/2-Regression/1-Tools/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder
diff --git a/...ression/1-Tools/solution/lesson_1-R.ipynb → ...ssion/1-Tools/solution/R/lesson_1-R.ipynb b/...ression/1-Tools/solution/lesson_1-R.ipynb → ...ssion/1-Tools/solution/R/lesson_1-R.ipynb
@@ -20,7 +20,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "#Build a regression model: Get started with R and Tidymodels for regression models"
+        "# Build a regression model: Get started with R and Tidymodels for regression models"
       ],
       "metadata": {
         "id": "YJUHCXqK57yz"
@@ -29,22 +29,22 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## Introduction to Regression - Lesson 1\r\n",
-        "\r\n",
-        "#### Putting it into perspective\r\n",
-        "\r\n",
-        "✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use `linear regression`, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use `logistic regression`. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.\r\n",
-        "\r\n",
-        "In this section, you will work with a [small dataset about diabetes](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.\r\n",
-        "\r\n",
-        "That said, let's get started on this task!\r\n",
-        "\r\n",
-        "<p >\r\n",
-        "   <img src=\"../images/encouRage.jpg\"\r\n",
-        "   width=\"630\"/>\r\n",
-        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n",
-        "\r\n",
-        "<!--![Artwork by \\@allison_horst](../images/encouRage.jpg)<br>Artwork by @allison_horst-->"
+        "## Introduction to Regression - Lesson 1\n",
+        "\n",
+        "#### Putting it into perspective\n",
+        "\n",
+        "✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use `linear regression`, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use `logistic regression`. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.\n",
+        "\n",
+        "In this section, you will work with a [small dataset about diabetes](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.\n",
+        "\n",
+        "That said, let's get started on this task!\n",
+        "\n",
+        "<p >\n",
+        "   <img src=\"../../images/encouRage.jpg\"\n",
+        "   width=\"630\"/>\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\n",
+        "\n",
+        "<!--![Artwork by \\@allison_horst](../../images/encouRage.jpg)<br>Artwork by @allison_horst-->"
       ],
       "metadata": {
         "id": "LWNNzfqd6feZ"
@@ -75,7 +75,7 @@
       "cell_type": "code",
       "execution_count": 2,
       "source": [
-        "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+        "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
         "pacman::p_load(tidyverse, tidymodels)"
       ],
       "outputs": [

diff --git a/2-Regression/1-Tools/solution/lesson_1.Rmd → 2-Regression/1-Tools/solution/R/lesson_1.Rmd b/2-Regression/1-Tools/solution/lesson_1.Rmd → 2-Regression/1-Tools/solution/R/lesson_1.Rmd
@@ -20,7 +20,7 @@ In this section, you will work with a [small dataset about diabetes](https://www
 
 That said, let's get started on this task!
 
-![Artwork by \@allison_horst](../images/encouRage.jpg){width="630"}
+![Artwork by \@allison_horst](../../images/encouRage.jpg){width="630"}
 
 ## 1. Loading up our tool set
 

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://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
 
-> ### [This lesson is available in R!](./solution/lesson_2-R.ipynb)
+> ### [This lesson is available in R!](./solution/R/lesson_2-R.ipynb)
 
 ## Introduction
 

diff --git a/2-Regression/2-Data/solution/Julia/README.md b/2-Regression/2-Data/solution/Julia/README.md
@@ -0,0 +1 @@
+This is a temporary placeholder