Add Steps in a Computer Vision Project Docs Page and Defining Your Computer Vision Project Docs Page (#13182)

Co-authored-by: RizwanMunawar <chr043416@gmail.com>

docs/en/guides/index.md

@@ -42,6 +42,8 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Edge TPU on Raspberry Pi](coral-edge-tpu-on-raspberry-pi.md): [Google Edge TPU](https://coral.ai/products/accelerator) accelerates YOLO inference on [Raspberry Pi](https://www.raspberrypi.com/).
 - [View Inference Images in a Terminal](view-results-in-terminal.md): Use VSCode's integrated terminal to view inference results when using Remote Tunnel or SSH sessions.
 - [OpenVINO Latency vs Throughput Modes](optimizing-openvino-latency-vs-throughput-modes.md) - Learn latency and throughput optimization techniques for peak YOLO inference performance.
+- [Steps of a Computer Vision Project ](steps-of-a-cv-project.md) 🚀 NEW: Learn about the key steps involved in a computer vision project, including defining goals, selecting models, preparing data, and evaluating results.
+- [Defining A Computer Vision Project's Goals](defining-project-goals.md) 🚀 NEW: Walk through how to effectively define clear and measurable goals for your computer vision project. Learn the importance of a well-defined problem statement and how it creates a roadmap for your project.
 
 ## Real-World Projects