0. done

docs/0. Overview/0.6. Resources.md

@@ -2,6 +2,10 @@
 
 ## Is there additional project resources?
 
+Yes, indeed! The cornerstone resource for this course is the MLOps Python Package. This repository is designed as a hands-on illustration of structuring an MLOps codebase. It leverages the dataset we’ve been discussing throughout the course, providing a glimpse into what your final project could resemble.
+
+For those keen on diving deeper into specific subjects, such as configuring VS Code or understanding Pydantic better, the course creators offer extensive insights through their personal blog. These posts explore these topics in greater depth.
+
 ## Can I suggest a new project resource?
 
-## Can I contribute to the resources?
+Certainly! The course operates on an open-source philosophy, welcoming contributions that enrich the learning journey. If you have resources to suggest or wish to highlight some that have already been discussed in the course, your input is valued and encouraged.

docs/0. Overview/index.md

@@ -1,5 +1,13 @@
 # 0. Overview
 
-This chapter will introduce the course and its content.
+This chapter provides a roadmap to the content of our course, aimed at developing your ability to craft durable Python codebases for AI/ML projects. It guides you from the initial steps of moving beyond notebook prototyping towards building structured Python packages and instilling high-quality coding and automation practices
 
-TODO
+Below is a summary with direct links to the main sections:
+
+- [0.0. Course](0.0. Course.md): Learn about the course's objective to bridge the gap between software development and data science, ensuring learners are equipped to handle AI/ML projects with advanced Python practices.
+- [0.1. Projects](0.1. Projects.md): Details on the default project and encouragement for learners to bring personal projects for a tailored learning experience.
+- [0.2. Datasets](0.2. Datasets.md): An explanation of dataset types, their role throughout the AI/ML project lifecycle, and guidance on dataset selection.
+- [0.3. Platforms](0.3. Platforms.md): Insights into choosing an MLOps platform based on organizational needs and the course's platform-agnostic approach.
+- [0.4. Mentoring](0.4. Mentoring.md): Information on available mentoring options by the course authors and the benefits of personalized guidance.
+- [0.5. Assistants](0.5. Assistants.md): Introduction to the course-specific OpenAI GPT-based assistant and guidelines for its use.
+- [0.6. Resources](0.6. Resources.md): Details on supplementary materials and how to contribute to the course's open-source resources.