I'm about to graduate in mid-February 2026 and I want to work as a machine learning engineer, LLM, or in data science. I've been consuming a ton of tutorials and feel like I'm stuck in tutorial hell. I know my way around tools like pandas, SQL, Power BI, and some machine learning algorithms, but I struggle with practical applications, especially deployment techniques involving FastAPI and Docker. I'm planning to spend the next 50 days focusing on concentrated project-based learning. My plan is to practice SQL and data processing, learn FastAPI, and tackle an end-to-end machine learning project that goes beyond just Jupyter notebooks. After that, I'd like to dive into LLM and RAG projects, and if I have time, explore PySpark or Airflow. Is this a realistic approach, and can I achieve this with 4-6 hours of dedicated work each day?
4 Answers
If you're putting in the effort, you'll gain a solid foundation in those areas. You can definitely create a couple of good starter projects. Plus, with the right approach, you'll be able to discuss these topics confidently during interviews.
Definitely! Focusing on building projects is key. Just make sure once you hit a rough patch, you resist the urge to jump back into a tutorial for just "one more lesson." That deployment stuff can be tricky at first, but getting your first API up and running will boost your confidence big time!
It's totally achievable as long as you manage burnout. I've learned far more from working on actual projects than from tutorials or courses!
One tip: try to connect your projects to a real-world problem or a story, even if you make something up. For instance, you could develop a tool that predicts when a garage needs spare parts. You can use an LLM to generate synthetic data for your model. This will keep you focused and prevent you from getting lost in the details.
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