The 5 levels of ML projects
Want to know what separates entry-level machine learning projects from the systems powering companies like Google and Amazon? The gap might seem impossibly wide, but there’s actually a clear progression most ML practitioners follow.
Today I’m mapping out the five levels of machine learning projects that separate complete beginners from industry leaders. By the end of this post, you’ll understand exactly where you are on this journey, and what specific skills you need to reach the next level.
Many aspiring ML Engineers get stuck building the wrong types of projects that never actually land them jobs. I’ll show you exactly what level of project you need for different roles — from entry-level positions to research teams at top AI companies.
Let’s start at the beginning. Level 1 is where every journey begins — working with clean, structured datasets in a Jupyter notebook on your laptop.
At this level, you’re downloading pre-cleaned datasets from sources like Kaggle. You’ll import libraries such as pandas for data manipulation, use matplotlib or seaborn — and maybe even Plotly for interactive visualizations — and experiment…