CPUR Learning Repository

An open, student friendly collection of coursework, practice material, workshop notes, datasets, and small projects created around learning at Career Point University (CPUR). The repository is intended to grow: students from any academic branch can contribute useful learning resources and projects.

Status: This is a mixed learning archive, not one installable application. Each project or exercise may have its own language, dependencies, and run instructions. Check its local README before running it.

Repository map

Area	What you will find
MCA/	MCA practice questions and coursework organized by semester: programming, web development, Java, R, open source, .NET, Android, and more.
CPUR-AGENTIC-AI-TRAINING/	Dated training material, notebooks, datasets, experiments, and projects covering Python, data science, ML, GenAI, and agentic AI.
MLOPS_WORKSHOP/	Workshop exercises and project material for MLOps; currently includes a FastAPI MLOps project under 00_DAY/.

The directory tree is the source of truth. Folder names and contents can evolve as contributors add material. Existing capitalization and spelling in paths are retained to avoid breaking links.

How the repository is organized

This repository is a learning monorepo: related educational materials live together, while individual projects remain in their own folders. There is no root-level build system or shared runtime. Most top-level areas have their own README; project-level READMEs should explain local setup and usage.

.
├── MCA/
│   ├── Practice_Questions_JAVA/
│   └── SEMESTERs/
│       ├── 01_sem/
│       ├── 02_sem/
│       └── 03_sem/
├── CPUR-AGENTIC-AI-TRAINING/
│   ├── 01_Phase/ ... 04_phase/
│   ├── kaggle-Dataset-Uploaded/
│   ├── My_Learning/
│   └── Projects/
└── MLOPS_WORKSHOP/
    └── 00_DAY/

Getting started

1. Browse the area README linked above and choose a topic or project.
2. Read any README in that folder for specific prerequisites and commands.
3. For notebooks, use Jupyter or the environment named by the notebook/project. For compiled or web projects, use the local project instructions. Do not assume dependencies are shared across folders.
4. Keep credentials, API keys, private data, and generated build files out of commits.

There are no repository-wide setup commands. Common tools across the collection include Python/Jupyter, Java, R, JavaScript/HTML/CSS, PHP, and .NET, but requirements vary by folder.

Contributing

Contributions from students across branches and disciplines are welcome: notes, solved exercises, labs, datasets with appropriate rights, tutorials, reproducible projects, and corrections all fit here. You do not need to work in MCA or AI to contribute.

1. Fork the repository and create a focused branch, for example docs/networking-lab or feature/library-management-demo.
2. Add material under the most relevant existing area. If none fits, create a clearly named topic folder and update the nearest README (and this map if adding a new top-level area).
3. Include a local README.md for a substantial project or topic. State its purpose, course/topic, prerequisites, setup/run steps, expected result, and any data or attribution requirements. Include only details you have verified.
4. Keep changes focused and use descriptive names. Prefer small examples that can be understood and run independently.
5. Review your diff, remove secrets and machine-specific files, then open a pull request describing the change and how you checked it.

Suggested contribution layout

MCA/SEMESTERs/03_sem/Networking/
├── README.md
├── notes.md
└── examples/
    └── subnet_calculator.py

Adapt the location and files to the material; this is a guide, not a required structure. For a new discipline, use a descriptive subject name rather than placing unrelated work in an existing subject folder.

Contribution expectations

• Share work you created or have permission to redistribute. Attribute external sources and datasets.
• Never commit passwords, tokens, private student information, or restricted course material.
• Explain assumptions, known limitations, and how to reproduce results.
• Keep academic solutions educational: add reasoning and context where possible, and follow your institution's academic integrity rules.
• Be respectful in reviews and issues. Reviewers may request clarification or changes before merging.

Open source and licensing

The repository is being prepared for community contributions, but a README alone does not grant reuse rights. No repository-wide license is declared here. Before publishing or accepting contributions, the maintainers should select and add a license that fits the intended reuse, and check whether existing files, datasets, images, and project dependencies may be redistributed. Until then, contributors should preserve source attribution and only submit material they are authorized to share.

Maintainers

This is a community learning archive maintained by its contributors. Use GitHub Issues for corrections or proposals and pull requests for changes (when hosted on GitHub).