The Summer of Machine Learning (SoM) is part of the BSoC open-source program, aimed at introducing contributors to AI and ML.
SoM in a nutshell:
- 7 Weeks + 2 Kaggle Hackathons + Projects
- Online
- Prerequisites: None
This is a structured, resource-driven introduction to Data Science and Machine Learning. Over seven weeks, you will go from Python basics all the way to deploying real ML models in the cloud and exploring deep learning. This year, two Kaggle Hackathons are included so participants get hands-on experience with actual ML competitions.
There is no shortage of free courses on the internet, which makes it genuinely hard to know where to start or what to skip. SoM cuts through that by pulling together a focused path from 25+ sources, so you are not spending time figuring out what to learn next. It is part of BitByte's broader community program, and the goal is to make ML accessible to anyone willing to put in the time.
| Week | Content |
|---|---|
| Week 0 | Pre-Program Setup |
| Week 1 | Python, Data Handling and Statistics |
| Week 2 | Data Preprocessing and Intro to ML |
| Week 3 | Core ML Algorithms and Model Evaluation |
| Week 4 | Ensemble Methods + Kaggle Hackathon I |
| Week 5 | Unsupervised Learning and Model Deployment |
| Week 6 | Neural Networks and Cloud Deployment |
| Week 7 | Specialized Topics + Final Kaggle Hackathon |
SoM is about making learning fun and interactive. We have a dedicated Discord server where you can interact with your peers, ask questions, and collaborate on projects. We also have a dedicated team of mentors who will guide you through your journey and help you with any doubts you may have.
So it's about learning, collaborating, and having fun! Just follow the course deligently and you will be good to go!
Two Kaggle Hackathons are part of the program this year, one at the midpoint and one at the end.
Hackathon I (Week 4): A mid-program competition where you apply skills from Weeks 1 to 4 on a real dataset. No prior competition experience needed.
Hackathon II (Week 7): An open-ended capstone challenge drawing from the full curriculum, ending with a live discussion on top solutions. A good one to keep in your portfolio.
A few ways to contribute to Summer of ML:
- Help peers on Discord & WhatsApp by sharing what you know.
- Improve or update the documentation.
- Suggest better resources for any week's content.
We are grateful to all the external material providers and websites that have helped us create this course. We would also like to thank our mentors for their guidance and support.
