An overview of the workflow from starting to launching an ML project
Essential terms that will pop up often during ML conversations
Overview of classification and regression goals
Understanding of some of the techniques you can use to optimize your ML model
- Curiosity about the machine learning project structure
Machine Learning has become an exciting route to go down by many teams and companies. However, it’s not always realistic that everyone is expected to catch up with all of the latest ML trends.
Usually Machine learning teams are made up of different people. On the technical side you can have a mixture of the different data scientists and engineers, like a Machine Learning Data Scientists, as well as Machine Learning and Data Engineers. The data scientists’ main responsibility would be building out or improving the models, and the engineers will help with everything else around deployment and that the models are getting the data they need.
From the non-technical side it’s likely you’ll have a project manager and possibly also several other business stakeholders. This course is aimed for these people, who need to understand what’s going on at a higher level, without necessarily having to dive into the technical components. Those that need to know enough to help with product vision, and be able to have and understand discussions about current statuses, blockers, as well as estimations.
In this course we’ll look at some of the different components involved in an ML project so that you can feel like you can have fruitful conversations when working on an ML project without needing to get bogged up on all the technical details.
Who this course is for:
- Anyone who wants to get a high-level overview of the different components involved in machine learning