Don’t Build an ML Portfolio Without These Projects

Building a strong machine learning portfolio requires several key project types to attract recruiters. It is recommended to start with 3-5 simple projects using varied algorithms and datasets to build a foundation. A crucial component is a comprehensive end-to-end project that demonstrates the full lifecycle from data collection and model training to deployment using tools like Docker and cloud platforms. To further stand out, candidates should include a research-focused project, such as re-implementing a paper, and write technical articles to solidify understanding and showcase communication skills.