Update README.md

README.md

@@ -1,59 +1,37 @@
 ---
+license: mit
 tags:
 - computer-vision
-- opencv
 - numpy
 - matplotlib
-- educational
-- beginner
-license: mit
+- opencv
+- beginner-friendly
 language: en
 ---
 
 # Computer Vision Learning Notebooks
 
 ## Summary
-This repository contains a series of **educational notebooks** that introduce the fundamentals of Computer Vision (CV) in a simple, visual, step-by-step way.  
-It is designed for both **technical and non-technical learners**, focusing on building intuition about how computers see and manipulate images.
-
-The notebooks progress from working with raw arrays (NumPy) to real images (PIL/OpenCV) and finally to drawing shapes, annotations, and simple overlays.
+A beginner-friendly collection of notebooks that introduce the **fundamentals of Computer Vision (CV)**.  
+Designed for both **technical and non-technical learners**, these notebooks focus on simple explanations, visual examples, and hands-on practice.
 
 ---
 
-## Notebooks Overview
-- **Notebook 1** → Introduction to images as arrays (NumPy basics).  
-- **Notebook 2** → Loading, resizing, and exploring color channels of real images.  
-- **Notebook 3** → Image transformations and manipulations (PIL / Pillow).  
-- **Notebook 4** → Drawing shapes, lines, and text on blank canvases and real images (OpenCV).
+## Current Notebooks
+- **Notebook 1** → Images as arrays with **NumPy** (understanding pixels).  
+- **Notebook 2** → Working with real images (loading, resizing, color channels).  
+- **Notebook 3** → Basic image transformations and manipulations.  
 
 ---
 
-## Intended Use
-These notebooks are ideal for:
-- Students beginning their journey in **Computer Vision**.  
-- Non-technical learners who want to **visualize how machines interpret images**.  
-- Developers looking for a clear, hands-on foundation before moving to advanced CV or deep learning topics.
+## Next Steps (In Progress)
+ 
 
 ---
 
-## Features
-- Beginner-friendly explanations paired with visual outputs.  
-- Bite-sized, progressive learning: **arrays → real images → drawing & annotations**.  
-- Uses standard libraries: **NumPy, Matplotlib, PIL/Pillow, OpenCV**.  
-- Each notebook contains concise theory, short code cells with comments, and visuals.
+## Who Is This For?
+- Students starting their CV journey.  
+- Non-technical learners curious about how machines see images.  
+- Developers looking for a smooth step before advanced CV/Deep Learning.  
 
 ---
-
-## How to Use
-1. Open the notebooks in order (1 → 4) for the intended learning progression.  
-2. Run each cell step-by-step and observe results.  
-3. Tweak parameters (e.g., rectangle coordinates, circle radius, colors) to experiment and learn.  
-4. Use copies of images (`.copy()`) when you want to preserve originals during edits.
-
----
-
-## Requirements (suggested)
-Install the basic packages used across the notebooks:
-
-```bash
-pip install numpy matplotlib pillow opencv-python