Your Machine Learning Project Roadmap:
Machine learning projects strive to create an impact, but there is often not a clear path to get there.
Navigating a successful project requires expertise in this highly technical field, up-to-date knowledge of research advances and toolkits, and a solid understanding of the data.
But there are unique intricacies when working with pathology and remote sensing data and in collaborating with the interdisciplinary teams involved. It takes many years to build the knowledge to be both efficient and effective in solving problems in these domains.
In this Machine Learning Roadmap you'll learn about 7 key components for a successful and impactful machine learning project:
- Clarity on available data and prediction task
- Review of related work to guide expectations
- Metrics that capture project objectives
- Data pipeline for labeling, preprocessing, and cleaning
- Model selection and training
- Validation, results analysis, and implications
- Iterate to generate new insights