Some general suggestions:

- At least occasionally read something that isn't on a screen (in a book).

- Do some experimentation on your own. Too many pretenders in this field parrot whatever they heard elsewhere. It's hard to argue with actual results.

- Don't mindlessly chase whatever's trendy: that's an endless treadmill and "the crowd" wastes a lot of time.

- Once you understand a few machine learning algorithms, you probably have enough. A bigger toolbox is better, but I recommend paying attention to things other than the machine learning activity itself, such as application of machine learning to the problem, data collection, data preparation and model validation.