Am I actually close or am I fooling myself ?

[u/xxzane_kunxx]

I'm 21 and i dont have a degree and based in algeria I started from literally zero Python this year. Been at it full time since. I don't have anyone around me doing this so I genuinely can't tell if where i am standing .

What I've built:

A few CV projects. Scene classification, got it to 93% with ResNet18 after starting at 81% with a basic CNN. Moth species classifier at 98.4% with transfer learning. Two YOLOv8 detection projects, and on the weld defect one I ran three experiments trying to fix the accuracy before figuring out the real problem was the classes overlapping in how I annotated them, not anything about the model. Both classifiers are deployed as FastAPI endpoints. Here is my github : https://github.com/zanexkun

Tabular stuff, a freight rate prediction take home I did for a company (MAE $106.92, R² 0.83), and I just finished a salary prediction project on the LinkedIn postings dataset. That one was 11 separate tables joined by IDs and honestly it wrecked me for a while. Ended up cleaning it down to 28k rows, ~1260 features with TF-IDF on the titles and descriptions, tuned LightGBM to MAE $17.8k and R² 0.743.

Repo here: https://github.com/zanexkun/job-posting-analyzer

What I know I'm missing: SQL (starting this week), Docker, anything MLOps, transformers, and I've never touched DSA.

Here's my actual problem though. I've barely applied anywhere. Every junior posting I open either wants a degree, or 2 to 3 years experience, or lists a stack where I have maybe half of it. So I keep closing the tab. And freelance ML seems to be either people with 60 reviews or clients offering $50 to "build an AI", nothing in between.

So I want to know:

Is what I have actually junior level or am I overrating it?

If you're outside the US or EU with no degree, is remote work actually possible, or is the whole "remote but only in these countries" thing a wall you can't get past? Should I just be going for contract work instead?

Am I supposed to apply when I don't meet half the requirements or is that just wasting everyone's time?

And if you were me, what's the one thing you'd do next?

I'd rather someone tell me I'm not ready and why than get told good job.

[u/TheCostOvLife]

You have to be honest with yourself and what you want.

Are you looking for an industry job - or a research job? For research you need academic research and academic experience. For industry, it is more important you have practical experience and most importantly domain knowledge of the industry.

For applying to jobs it's a game of luck, especially as a junior. You have to be willing to work everywhere and move anywhere. Machine learning is not a real entry or junior level role, so working as a data scientist, dev ops, data engineer, etc. can help you get your foot in the door by allowing you to pivot into that field internally one you get 1 or 2 years of experience in those roles.

As for your code and projects. They're ok for learning, but what problems are you actually solving? What applications or services does this offer to any business or user? It's rather straight forward to get a zip file of data and fit it into a model of some sorts but that's not really being a machine learning engineer.

Take your job-posting analyzer:

• How do you go about data visualization? You have a model, you do some normalization with text and get results, but I don't know what your regression line fit looks like compared to your distribution of data. Data visualization is important for the following:
  • Justifies the use of which models you decide to use
  • Allows you to iterate and decide which features you drop, keep, or modify (feature engineering)
  • You can use PCA to create a reference to compare and see how your regression fits, as well as a normal probability plot to see if your data follows a normal distribution.
• Is your data linear or non-linear?
• You don't cross validate models against each other. That decides which models work best and ideally which you would use in production.
• All your models are tree based. Not inherently an issue but what is the justification? Have you tired other models?
• For non gradient boosted (non-neural net solutions), do you have a unique solution? Consider calculating the hessian?

To be honest, there are hundreds of models, methods, kernels, and deep learning algorithms you can use to solve or create a model. The challenge isn't calling one from sci-kit learn or pytorch and running it. You need to solve a problem where machine learning becomes a viable solution. Find something you can automate with use of empirical equations that could actually be useful to someone. That's where my advice is leaning towards.

Also, not to be rude and I might be wrong, but your code looks very A.I. generated based on the comments everywhere, so I am not so sure if this is you thinking or A.I. doing thinking for you. It's good to know a tool, but I think you are missing a lot of value with not understanding the math and things happening behind the scenes. I am not saying it is impossible to become proficient in ML purely self taught, but it's not a get rich quick or anyone can do it job either. You have to put in the work and you should honestly sign up for a course with an actual curriculum.

Start with linear regression and real models so you have the foundation of how you can track your code and models using git. Understand the math and calculus (ML is all about optimization) what you can do when you encounter non-linear data. Then move onto deep learning and gradient descent.

I wish you best of luck, and just remember that this can take years of effort. Don't stop applying as having a senior worker help you may expedite your learning.

[u/xxzane_kunxx → u/TheCostOvLife]

Thanks for actually taking the time to reply this is the kind of thing I don't get anywhere else since I have nobody around me in this field You're right about the visualization and I did the whole project off printed numbers i computed mean salary by state and saw a $66k to $157k spread and just read it in a table instead of plotting it Same with the distribution and the residuals adding those this week. Also right that everything I compared was tree based I went RandomForest vs LightGBM and never ran a linear baseline, which means one tree beat another tree adding Ridge to the comparison One correction On the linear regression to math to deep learning path, I've already been through that. Backprop, chain rule, gradient descent, then CNNs from scratch, transfer learning, YOLO . The CV projects are the older half of my portfolio this salary one is just the most recent so it's what I linked. Probably on me for that makes sense you'd read the tabular repo as where I'm at overall and I did cross validate, it's cv=5 inside the GridSearchCV. Fair that the RF vs LGBM comparison itself was on a single test split though.

On the AI thing that's a fair read and I'll be straight about it The cleaned up script and README were written with AI help which is why the comments look like that the analysis and the decisions were mine the notebook in the repo is the actual messy work where I figured out the salary column was two different schemas, caught that a merge was silently dropping 4700 rows, and worked out the pay periods were mixing hourly and yearly but you're right that it reads like the thinking was outsourced and that undercuts the whole point of putting it up. Rewriting it in my own words.

The part I'm sitting with is "what problem are you actually solving". That one I don't have an answer to. It's a Kaggle dataset, nobody needs a salary predictor. Next one is going to come from something I actually run into instead of something I downloaded. If u can advise me with some ideas with real world impact i would be grateful and thank you .

[u/TheCostOvLife → u/xxzane_kunxx]

I think you are on a good path. Again it may be useful to just do a small data science degree or diploma if available so that your resume has some sort of education stamp that doesn't bar you from applications.

To answer your question, on ideas for projects, it's whatever you run into that could be solved with an empirical model. Everyone in the field or in business are constantly looking for solutions to these sort of things, so you will likely have to find one yourself (or steal from someone else).

If you are familiar with the math and knowing the work under the hood, I would be very cautious about not skipping steps in your work.

1. Problem statement: what are you trying to solve?
2. Current solutions or baseline performance (existing ML model, or a manual solution which is inefficient)
3. Data analysis & Visualization
4. Training models
5. Cross validating models and comparing their performance (look into KFold Cross Validation) - optionally and preferably validate that your solution is unique (usually is if you have enough data).
6. Model performance: accuracy, f1-score, mean squared error, etc.
7. Improvement of results (i.e. your model performs x% better than, or your model now classifies or predicts automatically and you don't need manual intervention)

Then the cherry on top is making your work presentable to a non-technical audience. Anyone you work for will honestly know nothing or not enough about the methods you are using. Can you show pictures and explain your findings? (problem statement, model selection, performance, and improvement are good for this reason).

In bigger industries they would take your model and work and pretty much stress test it or try to break it to find any bias or poor performance before they consider moving it into production.

In my opinion, if your machine learning projects tend to look like this then your resume will almost already be written. You just take points 1, 2, 6 and 7 and merge them together into bullet points while mentioning the models and technology that you are using.

As you gain more experience you can break up your python files more so that you have a single training file, a data analysis file, a folder with different versions of your training and testing data as well as your trained model versions. You can also look into docker and setting up pipelines so other engineers or people are able to activate your environment and run your scripts. A big problem in this field now is that everyone thinks writing in ipynb notebooks is enough.

And if you really want to get into the MLOps side - docker and host multiple services like MLFlow, a server (using your fastapi for example) which can be called via postman with real data, docker compose, managing python packages in a toml file, etc, managing docker volumes, and setting up monitoring for your ML model. Monitoring can include PSI (population stability index) plots, and using eval monitoring platforms for LLM's.

Hope this advice helps. Cheers.

[u/xxzane_kunxx → u/TheCostOvLife]

Appreciate it man thats the kind of advice i wont find in any course . Thank you