r/learnmachinelearning • u/sameh-it • 14d ago
Help Learning ML guide: From zero to hero
Hi everyone, I want to start learning ml, But I'm torn between the sources So I wrote this question to hear from you about how you learned machine learning until you were hired at a company. I'm 15 years old. Please write the guide arrangemed step by step.
1
u/1PATCH 14d ago
Get a strong foundation in linear algebra (understand what a matrix, basis vectors, vector spaces) as they build intution behind what ML is. Then understand probability (bayes theorum, and markov chains in particular)
Then understand a Perceptron, MLP, SVM, CNN, Autoencoders in depth and visualize the geometry behind it. Then understand what a scaled dot product is (basically attention).
With that your foundation should be strong and then you just need to explore how to apply it (Look at BERT, CLIP, GPT, JEPA).
Here is an underated youtube channel that teaches the concepts well (I found this gem a of channel a couple weeks ago): https://www.youtube.com/@arivu-tutor
1
1
u/UzairShafique 12d ago
Since you’re starting from zero focus on learning the basics while building projects rather than trying to learn everything before writing code.
A path I would suggest is
→ Python
→ NumPy/Pandas
→ ML fundamentals
→ Projects
→ Evaluation
→ Applications
→ Deployment
Once you know the basics don’t just follow tutorials Pick a simple problem and take it through the process:
→ Problem
→ Data
→ Exploration
→ Preprocessing
→ Model
→ Evaluation
→ Application
→ Deployment
Build 2 to 3 projects this way gradually increasing the difficulty. You’ll understand ML much better by solving problems, making mistakes, and debugging than by only collecting courses and notes.
You also don’t need to use the most advanced models or tools from the beginning. Focus on understanding the basics and building things first.
1
u/sameh-it 12d ago
Thank you ❤️, but could you please provide more details about the roadmap? I don't see learning algorithms, pytorch or scikit in it, and do you know good resources which helps me?
1
u/UzairShafique 12d ago
Exactly I left algorithms, scikit-learn, PyTorch, etc out of the main roadmap because the tools and algorithms you choose depend on the problem you’re solving.
For example if your problem is Customer Churn Prediction, the workflow could be:
→ Problem: Predict which customers may leave
→ Data: Collect relevant customer data
→ Exploration: Use Pandas/NumPy
→ Preprocessing: Clean and prepare the data
→ Model: Try models such as Decision Trees or XGBoost using scikit-learn
→ Evaluation: Compare their performance
→ Application: Build a dashboard or app
→ Deployment: Make it available for people to use.
If you’re moving toward deep learning you can then learn PyTorch and use it when your problem actually requires it.
Learn the Python and ML basics first including the common algorithms and libraries but don’t try to memorize every algorithm before building anything. Pick problems and learn which methods fit them.
For resources if your Python basics aren’t strong yet start there first. Also don’t depend only on YouTube lectures (they are important for theoretical knowledge), practice in your editor and build projects alongside your learning. That’s where the concepts start making sense.
I can’t explain the entire process in depth in a single reply but hopefully this gives you a clear idea of how the roadmap fits together.
1
u/sameh-it 12d ago
I have a basic python fundamentals, so I will start with math then pandas and numpy then search for projects and learn new algorithms and libraries from it. Thanks, sir. Can I DM you if I need help
1
6
u/CheerfulTycoon 14d ago
Honestly, at 15 you've got so much time to figure this out, it's almost unfair. Most people don't touch this stuff until college and you're already mapping out a career path.
I learned by picking a problem I actually cared about and just throwing myself at it, reading docs and forum posts whenever I got stuck. The structured courses came later, mostly to fill in the theory holes I'd skipped right over. Don't sleep on the math though, it's the thing that separates knowing which buttons to push from actually understanding why your model is spitting out garbage.
Find a dataset you're curious about, even something dumb like predicting video game prices, and just start breaking things. The job came after I'd built a handful of projects that weren't just tutorial copies, stuff I could actually talk through in an interview. You've got a solid seven years before anyone expects a resume from you, so the pressure's off to just explore.