One of MLC's missions is to redefine research training (think about a PhD, postdoc, early faculty, or entry level research scientist training where one is growing gradually in their maturity to become a researcher) by experimenting moving such a process outside of academic and industrial institutions. The reason is obvious: the traditional, archaic, and narrow ML research entryway can no longer accommodate the exploding need both from the market to profit (from ML products), and from individuals to thrive and grow.

Not to mention expanding the entrance is the _only_ way to allow real diversity in the field.

The downside? The lack of structure and institutional support can easily make one feel lost. This *DIY PhD*, if you may, while flexible and freeing, can also be disorienting, if you don't keep your goals and plans in check regularly.

Since we are not your employer (or parents, for that matter), we are not going to mandate that you submit a research report every month to track your progress. And here is the best part: *you get to cultivate your own growth, and learn to be responsible, reliable, productive and happy along the way.* Here you get to build an entire curriculum and training program of your own; what we do, is only laying out all the possible puzzle pieces for you to start designing. 

Now, what is a DIY research training look like?

It can really be as simple or as complicated as you'd like. Let me show two examples in both cases.

Example 1: a simple path to publish your first paper.