Can you share some pointers to get better at working alone in projects?
ML is an extremely broad subject but generally it can be understood broadly as follows


you formalize the problem 
you identify the domain of the problem 
you identify candidate models to solve the problem 
you evaluate the candidate models with data subsets 
you select a model and try to fit your data
you deploy the model


You can think of it as a(n iterative deepening ) search of a tree of choices.  To improve your odds of success (find a choice that solves the problem) you need the tree narrow down as quickly as possible e.g. pruning bad choices.  This is only acquired through experience but having a broad understanding of models helps e.g by taking introductory courses.

Once you've narrowed down the types of models you want to apply.  You want to know if you have the expertise to apply them.  Given that this is a broad subject, you acquire expertise only in a limited set of models.  If the model that best fits your problem is not in your domain of expertise you either refer it someone with that expertise or reject that you can solve the problem in a limited amount of time e.g. due to the complexity of the tree of choices for a non-expert.

Within your model expertise you'd have acquired a set of techniques to determine that even when you have the right model, if you do not have sufficient data, or data with sufficient quality, or the necessary computation resources you may not be able to deploy your model.