added lesson_3.html, fixed links and image refs

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
 

README.md

@@ -96,7 +96,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 |      04       |                Techniques for machine learning                 |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
 |      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |         <ul><li>[Python](2-Regression/1-Tools/README.md)</li><li>[R](2-Regression/1-Tools/solution/R/lesson_1.html)</li></ul>         |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
 |      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |          <ul><li>[Python](2-Regression/2-Data/README.md)</li><li>[R](2-Regression/2-Data/solution/R/lesson_2.html)</li></ul>          |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
-|      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |        <ul><li>[Python](2-Regression/3-Linear/README.md)</li><li>[R](2-Regression/3-Linear/solution/R/lesson_3-R.ipynb)</li></ul>        |      <ul><li>Jen and Dmitry</li><li>Eric Wanjau</li></ul>       |
+|      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |        <ul><li>[Python](2-Regression/3-Linear/README.md)</li><li>[R](2-Regression/3-Linear/solution/R/lesson_3.html)</li></ul>        |      <ul><li>Jen and Dmitry</li><li>Eric Wanjau</li></ul>       |
 |      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |     <ul><li>[Python](2-Regression/4-Logistic/README.md) </li><li>[R](2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb)</li></ul>      |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
 |      09       |                          A Web App 🔌                          |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
 |      10       |                 Introduction to classification                 |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            | <ul><li> [Python](4-Classification/1-Introduction/README.md) </li><li>[R](4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb)  | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |