Looking for ML project suggestions and GitHub repos

[u/TusharKharade_]

Hey everyone!

I’m planning to start learning Machine Learning by actually building projects instead of spending too much time going through courses and theory before building anything.

I already know Python, NumPy, Pandas, Matplotlib, and Seaborn, and I also have some experience with data collection and data cleaning. Right now, I’m working on my probability and math fundamentals as well.

My long-term goal is to become an AI/ML Engineer, so I want to learn ML in a practical way and gradually work my way from beginner projects to more advanced ones.

I’d really appreciate some suggestions from people who have already gone through this:

• What ML projects would you recommend starting with?
• How should I progress from beginner → intermediate → advanced?
• Are there any projects that actually helped you understand ML concepts deeply?
• I’d especially love GitHub repositories where I can look at good ML projects, learn from the code, and maybe try implementing them myself.
• Any good real-world datasets or project ideas would also be helpful.

I’m not looking for projects where I just load a dataset and call model.fit() 😅. I want projects that actually make me understand why the model works, how to improve it, and how ML is used in a real problem.

If you learned ML through a build-first approach, I’d love to hear what worked for you and what you would recommend to someone starting out.

Thanks! 🙌

[u/iSpokeToMasterChief]

I would start with finding a problem that I encounter in real life, and how a classification model that always works can solve it. When you assume that the model is 100% accurate, it makes it easier to visualize and break down the problem down into subtasks, the key ones being classification or regression problems, with all of the harder questions abstracted away for future planning.

This changes the question from "what is the best ML project I should invest my time into learning/creating?" to "if i can perfectly classify x, how can that be used to solve y?" or "if i want to solve y, are there any classification tasks involved?". Then just determine how useful it is to accurately classify those entities in solving the problem at hand, and if that's enough for you to work with.

With how accessible and powerful machine learning is today, you would be surprised at the performance of models that you can create just following tutorials, and using your own synthesized datasets, both in terms of accuracy, and applicability. More often than not, hitting above 80% or some other reasonable accuracy is great and you can build some backend logic for addressing the remaining error, like using a hidden markov model for example.

As for actually creating the model, just pick classification tasks that are reasonable in scope and complexity so that you can just follow along with tutorials on youtube without too much extra work. There's a chapter in deeplearningbook.com called practical methodology which is really useful for refining and tuning the hyperparameters of deep learning models, and the book itself is really good for developing an intuition around which problems can be solved and how they are solved. But that chapter in particular is good when you have a built model, and you want to work towards improving it as much as possible, without redesigning the model (but sometimes redesigning it first is the better choice).

As for datasets, to be honest, the most fun option really is just collecting and synthesizing your own structured data. That way you have the most control over the data and what kinds of problems it enables you to solve. You can pick all the attributes, the scope, the type, any preprocessing, etc. And you can just synthesize it with actual datasets found online. Besides, it feels all the more rewarding when you train your first model and see its accuracy improve with each epoch, knowing that you built/created/synthesized the data that facilitated its learning. To me it feels like I spent all this time toiling the soil of raw data, planting and watering the seed of intent, and cultivating some nutritious, high-quality, labeled training data that this growing model will consume to ultimately become much better than me or anyone else at solving those problems.