We're looking to build a machine learning algorithm which can create a model and predict a result for data sets that include time and make sure we take the time of every data point into account.
For example, if we have an application that collects information all the time. The information can be different data points at different points in time.
We then want to predict the likelihood of an event every hour based on the information we collected.
To create the model, we'll of course collect information about those events we want to predict in order to allow the algorithm to learn.
I will share a document for further details and exact scope.
What we want in proposals:
1. Your ML background, past projects, examples of work you've done
2. Your approach to our problem.
3. How much do you want for the full scope (design + implementation). Proposals for only one of those will not be considered. This is going to be a fixed scope project and not time and materials.
Consider in the scope of the project there's the design, implementation and supporting us to run your algorithm (including any dependencies, libraries etc) and how do we connect into it to send it data and predict from it, making sure the algorithm stored the data in the right place etc.
*Ignore the budget in here. Submit how much you're proposing for the full scope.
4. Expected timelines for each part of the project (consider and plan the time to support us as well)
Shortlisted candidates will be sent some follow up questions. Make sure you specify sections 1-4 in your proposal.
About the recuiterMember since Mar 14, 2020 Resolute Softte
from Ceara, Brazil