update docs front page

docs/index.md

@@ -15,21 +15,21 @@ The following contents are listed in suggested chronological order.
 
 But feel free to mix in match in anyway you feel fit.
 
-| **Resource** | **Description** |
-|--------------|-----------------|
-| [A 6 step framework for approaching machine learning projects](https://dev.mrdbourke.com/zero-to-mastery-ml/a-6-step-framework-for-approaching-machine-learning-projects/) | A guideline for different kinds of machine learning projects and how to break them down into smaller steps. |
-| [Introduction to NumPy](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-numpy/) | NumPy stands for Numerical Python. It's one of the most used Python libraries for numerical processing (which is what much of data science and machine learning is). | 
-| [Introduction to pandas](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-pandas/) | pandas is a Python library for manipulating and analysing data. You can imagine pandas as a programmatic form of an Excel spreadsheet. |
-| [Introduction to Matplotlib](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-matplotlib/) | Matplotlib helps to visualize data. You can create plots and graphs programmatically based on various data sources. |
-| [Introduction to Scikit-Learn](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-scikit-learn/) | Scikit-Learn or sklearn is full of data processing techniques as well as pre-built machine learning algorithms for many different tasks. |
-| [Milestone Project 1: End-to-end Heart Disease Classification](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-heart-disease-classification/) | Here we'll put together everything we've gone through in the previous sections to create a machine learning model that is capable of classifying if someone has heart disease or not based on their health characteristics. We'll start with a raw dataset and work through performing an exploratory data analysis (EDA) on it before trying out several different machine learning models to see which performs best. | 
-| [Introduction to TensorFlow/Keras and Deep Learning](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-dog-vision-v2/) | TensorFlow/Keras are deep learning frameworks written in Python. Originally created by Google and are now open-source. These frameworks allow you to build and train neural networks, one of the most powerful kinds of machine learning models. In this section we'll learn about deep learning and TensorFlow/Keras by building Dog Vision 🐶👁️, a neural network to identify dog breeds in images. |
-| [Communicating your work](https://dev.mrdbourke.com/zero-to-mastery-ml/communicating-your-work/) | One of the most important parts of machine learning and any software project is communicating what you've found/done. This module takes the learnings from the previous sections and gives tips and tricks on how you can communicate your work to others. |
+| **Section** | **Resource** | **Description** |
+|-----|-----|-----|
+| 00 | [A 6 step framework for approaching machine learning projects](https://dev.mrdbourke.com/zero-to-mastery-ml/a-6-step-framework-for-approaching-machine-learning-projects/) | A guideline for different kinds of machine learning projects and how to break them down into smaller steps. |
+| 01 | [Introduction to NumPy](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-numpy/) | NumPy stands for Numerical Python. It's one of the most used Python libraries for numerical processing (which is what much of data science and machine learning is). | 
+| 02 | [Introduction to pandas](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-pandas/) | pandas is a Python library for manipulating and analysing data. You can imagine pandas as a programmatic form of an Excel spreadsheet. |
+| 03 | [Introduction to Matplotlib](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-matplotlib/) | Matplotlib helps to visualize data. You can create plots and graphs programmatically based on various data sources. |
+| 04 | [Introduction to Scikit-Learn](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-scikit-learn/) | Scikit-Learn or sklearn is full of data processing techniques as well as pre-built machine learning algorithms for many different tasks. |
+| 05 | [Milestone Project 1: End-to-end Heart Disease Classification](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-heart-disease-classification/) | Here we'll put together everything we've gone through in the previous sections to create a machine learning model that is capable of classifying if someone has heart disease or not based on their health characteristics. We'll start with a raw dataset and work through performing an exploratory data analysis (EDA) on it before trying out several different machine learning models to see which performs best. | 
+| 06 | [Introduction to TensorFlow/Keras and Deep Learning](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-dog-vision-v2/) | TensorFlow/Keras are deep learning frameworks written in Python. Originally created by Google and are now open-source. These frameworks allow you to build and train neural networks, one of the most powerful kinds of machine learning models. In this section we'll learn about deep learning and TensorFlow/Keras by building Dog Vision 🐶👁️, a neural network to identify dog breeds in images. |
+| 07 | [Communicating your work](https://dev.mrdbourke.com/zero-to-mastery-ml/communicating-your-work/) | One of the most important parts of machine learning and any software project is communicating what you've found/done. This module takes the learnings from the previous sections and gives tips and tricks on how you can communicate your work to others. |
 
 If you have any questions, leave an issue/discussion on the [course GitHub](https://github.com/mrdbourke/zero-to-mastery-ml/).
 
 ## Author
 
 [Daniel Bourke](https://www.mrdbourke.com)
 
-Last update: 6 September 2024
+Last update: 24 September 2024