image linking tidy-up, temporary fix for docsify

2-Regression/1-Tools/README.md

@@ -1,6 +1,6 @@
 # Get started with Python and Scikit-Learn for Regression models
 
-![Logistic vs. Linear Regression Infographic](https://github.com/jlooper/ml-for-beginners/blob/main/2-Regression/1-Tools/images/Linear%20vs%20Logistic%20Regression.png)
+![Logistic vs. Linear Regression Infographic](./images/logistic-linear.png)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)

2-Regression/2-Data/README.md

@@ -1,6 +1,6 @@
 # Build a Regression Model using Scikit-Learn: Prepare and Visualize Data
 
-> ![Data Vizualization Inforgraphic](https://github.com/jlooper/ml-for-beginners/blob/main/2-Regression/2-Data/images/3-1-Data_Visualization.png)
+> ![Data Vizualization Infographic](./images/data-visualization.png)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)

2-Regression/3-Linear/README.md

@@ -1,9 +1,9 @@
 # Build a Regression Model using Scikit-Learn: Regression Two Ways
 
-![Linear vs Polynomial Regression Infographic](https://github.com/jlooper/ml-for-beginners/blob/main/2-Regression/3-Linear/images/3-1-Linear_Vs_Polynomial_Regression_.png)
+![Linear vs Polynomial Regression Infographic](./images/linear-polynomial.png)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
-### Introduction
+### Introduction 
 
 So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this unit. You have also visualized it using Matplotlib. Now you are ready to dive deeper into regression for ML. In this lesson, you will learn more about two types of regression: basic linear regression and polynomial regression, along with some of the math underlying these techniques.
 

2-Regression/4-Logistic/README.md

@@ -31,7 +31,7 @@ Logistic Regression does not offer the same features as Linear Regression. The f
 
 There are other types of Logistic Regression, including Multinomial and Ordinal. Multinomial involves having more than one categories - "Orange, White, and Striped". Ordinal involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
 
-![Multinomial vs Ordinal](https://github.com/jlooper/ml-for-beginners/blob/main/2-Regression/4-Logistic/images/Multinomial_Vs_Ordinal.png)
+![Multinomial vs Ordinal Regression](./images/multinomial-ordinal.png)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ### It's Still Linear

README.md

@@ -84,7 +84,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 |      21       |          Power Usage in India ⚡️          |               [Time Series]()                | tbd                                                                                                        |                      [lesson]()                       | Francesca |
 |      22       |  Introduction to Reinforcement Learning  |          [Reinforcement Learning]()          | tbd                                                                                                        |                      [lesson]()                       |  Dmitry   |
 |      23       |       Help Peter avoid the Wolf! 🐺       |          [Reinforcement Learning]()          | tbd                                                                                                        |                      [lesson]()                       |  Dmitry   |
-|      24       | Real-World ML Scenarios and Applications |        The Future of Machine Learning        | Interesting and Revealing real-world applications of ML                                                    |    [lesson](8-Real-World/2-Applications/README.md)    |    All    |
+|      24       | Real-World ML Scenarios and Applications |        ML in the Wild        | Interesting and Revealing real-world applications of classical ML                                                    |    [lesson](8-Real-World/2-Applications/README.md)    |    All    |
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index.html

@@ -3,7 +3,7 @@
 
 <head>
   <meta charset="UTF-8">
-  <title>Document</title>
+  <title>Machine Learning for Beginners</title>
   <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
   <meta name="description" content="Description">
   <meta name="viewport"
@@ -15,8 +15,8 @@
   <div id="app"></div>
   <script>
     window.$docsify = {
-      name: 'Your Curriculum Name',
+      name: 'Machine Learning for Beginners',
-      repo: 'https://github.com/microsoft/curriculum-repo-name',
+      repo: 'https://github.com/jlooper/ml-for-beginners',
       relativePath: true
     }
   </script>