Adding my two cents, honestly, the market for junior roles is pretty brutal right now. I spent months trying to break into ML, but unfortunately, the entry-level requirements are just bloated. I had issues with some of the generic bootcamps; they promise the moon but leave you without a real portfolio. You should definitely prioritize getting comfortable with MLOps or data engineering, because pure model building is kinda saturated.

• Check out the fast.ai courses for practical ML workflows, they are way better than the paid stuff.
• Learn to use NVIDIA RTX 4090 24GB GDDR6X if you plan on local training, but seriously, just use cloud credits first to save your cash.
• Read the documentation for Google TensorFlow 2.15.0 framework extensively; it is not as good as expected for beginners but essential for the industry. Quick tip: Stop chasing the perfect title. Build a project that actually solves a messy, real-world data problem instead of just cleaning datasets from Kaggle. My biggest mistake was focusing on theory instead of deployment. If you can show a recruiter a live dashboard or an API that actually works, you are already ahead of 90% of the applicants. The tooling is the hard part, not the math. Just pick one stack and stick to it until you break something. Good luck out there, you're gonna need it.

Coming back to this, you really dont want to burn through your whole 2k budget on a generic bootcamp that wont get you hired. I'd suggest focusing on building a rock-solid portfolio instead. That counts for way more than some random certificate. Here is how I would spend that cash to actually get job-ready:

• Grab a used workstation if your current rig is slow; something like a Dell Precision 7540 32GB RAM 512GB SSD can handle heavy local processing without breaking the bank.
• Use the remaining budget on a specialized subscription like DataCamp or a high-end course on deep learning from a reputable source.
• Spend your time building projects on GitHub that solve real problems, not just copying Titanic datasets. Be careful not to fall for the hype of those expensive degree programs. Focus on the tools and you'll be fine.

Honestly, 6 months is a tight window, but you can definitely make it happen if you focus. In my experience, dont overthink the stability part too much right now. Most juniors just need to get their foot in the door.

• Data Science: Usually more stable since every company has data that needs cleaning and basic dashboarding. Youll be doing a lot of SQL and business communication.
• Machine Learning: Way higher ceiling, but yeah, it is harder to land that first role. You need a rock-solid grasp of math and coding, not just library usage. If I were you, I would spend that 2k on a solid cloud cert like the [[Amazon AWS Certified Solutions Architect Associate]] to build a foundation. Keep building projects with [[NVIDIA GeForce RTX 4090 24GB GDDR6X]] if you are serious about ML. Stop spinning your wheels and just pick the one that feels less like a chore to study.