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

@@ -1,6 +1,6 @@
 # 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 

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    |
 ## Offline access
 
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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',
-      repo: 'https://github.com/microsoft/curriculum-repo-name',
+      name: 'Machine Learning for Beginners',
+      repo: 'https://github.com/jlooper/ml-for-beginners',
       relativePath: true
     }
   </script>