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title: Bring Your Own Project 2026 (1y)

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# Bring Your Own Project 2026 (1y)
## A chance to get your hands on ML

DSG is hosting an exciting event focused on learning and coding! As part of the event, you’ll need to submit an individual proposal outlining the project idea you’d like to work on, following the provided guidelines. Selected proposals will move forward to the month-long coding phase, where you can bring your ideas to life. Finally you will be able to present and showcase this project to all! 

Start drafting your proposals to showcase your skills and understanding of your chosen field. While we don’t expect you to come up with completely new ideas, we encourage you to apply your own unique approaches to the problem you choose. To strengthen your proposal, we encourage you to read relevant blogs, articles, and research papers to make sure that your idea has a concrete foundation which will help you create an in-depth and well researched proposal.

<!-- Since ETEs are just around the corner, we encourage you to familiarize yourself with the resources provided to build a solid understanding of machine learning fundamentals. You may start identifying areas of interest and think about potential project ideas. You should explore relevant articles, blogs, and research papers to deepen your understanding and refine your project concept after which you should draft your proposal, after the ETEs. Further details on the proposal such as format and guidelines will be shared with you soon. -->



**Sample Ideas (you can think along these lines)**

* Mood-Based Music Selector: Design a basic app that uses a form to gauge a user’s mood and recommend a playlist that fits their mood.
* Feature Extraction system: Extracts the most relevant and informative characteristics from data.
* Image Captioning Model: Develop a model that can generate descriptive captions for images.
* Stock Price Movement Prediction: Predict stock price trends or movements based on historical stock data.
* Chess Bot: Create a model that plays chess and evaluates positions using Machine Learning. 

**Proposal Guidelines**
* Your will be required to submit a text proposal as well as a short video explaining the proposal. 
* Try to answer every section in the proposal format in a detailed manner. We don't expect you all to have an exact idea on how to carry out the project, what we do expect is a general timeline and direction with which you're going ahead. A sample proposal has been provided as an inspiration. 
* Along with your written proposal, you’ll need to create a short video presentation (about 2 minutes) explaining your project idea. The goal of the video is to clearly communicate what your project is about, why it’s interesting, and how you plan to approach it.

**Some of the projects from last year's BYOP:** 

[BYOP'24 Projects](https://github.com/Atm1801/BYOP-24)
[BYOP'25 Projects](https://github.com/ManjotSingh08x/BYOP_25)

**Proposal Format:**
[Proposal Format](https://docs.google.com/document/d/1plVzCCnrpOBSRuiSzUmb_b8eoGDpe8AW2eBX1FbMKZ0/edit?usp=sharing)

**Sample Proposal:**
[Sample Proposal](https://drive.google.com/file/d/1ZMRwdBRHLBz8K4tykRPaMaqEdKj7pjNK/view?usp=sharing)
**Sample Proposal Video:**
[Sample Proposal Video](https://drive.google.com/file/d/1YsPANRXh_pASGg0xCfVuweNQYy1m6PE_/view?usp=sharing)

**Timeline**

* 29th October: **Proposal submission begins.**
* 1st December: **Proposal submission ends.**
* 5th December: **Onboarding of selected candidates.**
* 22th December: **Mid-Term Evaluation**
* 5th January: **Final Report and Presentation Submission**
<!-- * 10th December: **Onboarding of selected candidates.**
* 28th December: **Mid-Term Evaluation.**
* 15th January: **Final Report Submission.** -->



**Project Guidelines**

* The end deliverables of BYOP will be a github repo and a presentation of the project. We will be providing resources for the same, that you can refer to.
* We expect you to start your coding phase after the mid evaluation, if not before. (you can use the period before the mid evaluation to understand the concepts that are being used in your projects).

* You can always refer to the resources provided below as well as on our linktree: https://linktr.ee/dsgiitr. You can even reach out to us on Discord or Instagram or through our Open Community.

**Submission Form:** [Submit Here!](https://docs.google.com/forms/d/e/1FAIpQLSdWP9BuW-ih3Y7pIRyh_ShmcdJENufxymUFe3FevMBVN31mMw/viewform?usp=header)

# Resources

Here are some beginner-friendly resources to help you explore and navigate ideas for your BYOP project proposal-

### Python
* Corey Schafer's Python Basics Playlist: [Watch Here](https://youtube.com/playlist?list=PL-osiE80TeTt2d9bfVyTiXJA-UTHn6WwU&si=1y8zW3DsvkXgY5RJ)
* Corey Schafer's Object-Oriented Programming (OOP) Playlist: [Watch Here](https://youtube.com/playlist?list=PL-osiE80TeTsqhIuOqKhwlXsIBIdSeYtc&si=QTffk0RoXvUQiSZN)

Corey Schafer's playlists are highly recommended for learning Python, including both Python basics and object-oriented programming.

**Documentation References:**

In the world of Python, it's essential to become skilled at searching for information when needed, as it can be challenging to remember all libraries. Here are some documentation resources you should have at your fingertips for quick reference:

[Python Official Documentation](https://www.python.org/doc/)
[NumPy Documentation](https://numpy.org/doc/)
[Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/)
[scikit-learn Documentation](https://scikit-learn.org/stable/index.html)
[Pytorch Documentation](https://docs.pytorch.org/docs/stable/index.html)

### Machine learning 

* Machine Learning Specialisation by Andrew NG: [Watch here](https://www.coursera.org/specializations/machine-learning-introduction?) 
This is an excellent beginner-friendly course and you can apply for financial aid to access it for free.

* Subsequently, you can move on to an advanced Machine Learning playlist which will provide you with a comprehensive understanding of how machine learning algorithms work in the ML domain: [CS229](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU). The first 12 lectures should be sufficient for a good understanding of the fundamentals.
* For hands-on coding of machine learning algorithms using Scikit-Learn, refer to this YouTube playlist: [Machine Learning with Scikit-Learn.](https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw) or [Hands on Machine Learning](http://14.139.161.31/OddSem-0822-1122/Hands-On_Machine_Learning_with_Scikit-Learn-Keras-and-TensorFlow-2nd-Edition-Aurelien-Geron.pdf)

This learning path will equip you with a solid foundation in machine learning, practical coding skills, and the ability to tackle real-world ML projects.

### Deep Learning 

* [3Blue1Brown Playlist](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi): A very intuitive playlist for basic deep learning concepts. The first 4 videos are enough before moving on to the advanced concepts 
* Follow the [Deep Learning course](https://youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) diligently to gain a comprehensive understanding of deep learning.(Lec01-Lec13)
* For PyTorch learning, you can choose either [Playlist 1](https://youtube.com/playlist?list=PLhhyoLH6IjfxeoooqP9rhU3HJIAVAJ3Vz) or [Playlist 2](https://youtube.com/playlist?list=PLqnslRFeH2UrcDBWF5mfPGpqQDSta6VK4), depending on your preferences.

For more resources and background mathematical knowledge, you can follow the [Roadmap for Data Science by DSG](https://hackmd.io/@mvLTsj_2ThajfZlv7B0fSw/SyJsjY4gp)

Begin by focusing on foundational skills, such as Python programming and introductory machine learning concepts. Establishing a solid foundation will allow you to gradually expand your expertise and address more complex aspects of your project over time.

### Open Community
Join our [Open Community](https://chat.whatsapp.com/KZu3EkEmfqLAAzvXTrIljW?mode=ems_copy_t) for any further doubts or queries.