update README + docs

README.md

@@ -16,6 +16,26 @@ Welcome! This repository contains all of the code, notebooks, images and other m
 * **12 September 2024** - Working on updating the materials for 2025, see progress in [#105](https://github.com/mrdbourke/zero-to-mastery-ml/discussions/105)
 * **12 October 2023** - Created an online book version of the course materials, see: https://dev.mrdbourke.com/zero-to-mastery-ml/ 
 
+## Contents
+
+The following contents are listed in suggested chronological order.
+
+But feel free to mix in match in anyway you feel fit.
+
+> **Note:** All of the datasets we use in the course are available in the [`data/`](https://github.com/mrdbourke/zero-to-mastery-ml/tree/master/data) folder.
+
+| **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 | [Milestone Project 2: End-to-end Bulldozer Price Prediction](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression.ipynb) | In this project we'll work with an open-source dataset of bulldozer sales information. We'll use this data to build a machine learning model capable of predicting the sales price of a bulldozer based on several input parameters such as size and brand. Since this dataset isn't perfect, we'll work through several data preprocessing steps before building a model. And since we'll be working towards predicting a number (price of bulldozers), this project is known as regression project. | 
+| 07 | [Milestone Project 3: 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. |
+| 08 | [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. |
+
 ## What this course focuses on
 
 1. Create a framework for working through problems ([6 step machine learning modelling framework](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-1-getting-ready-for-machine-learning/a-6-step-framework-for-approaching-machine-learning-projects.md))

docs/index.md

@@ -11,15 +11,20 @@ This webpage contains all of the materials for the Zero to Mastery Data Science
 
 ## Contents
 
+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 ons how you can communicate your work to others. |
+| [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/).