rakmial

This is only half true. 401k matching typically takes time to vest, and you don't have the same amount of personal control over a 401k. Also, particularly in tech, if you move from a larger company with 401k to a small company with no benefits plan in place, you're not going to be able to roll the 401k stuff into your IRA. Obviously it won't just disappear, but you'll have just leave it in your previous employer's care.

Roth IRAs can be borrowed against for qualified expenses, like buying a home or paying for sudden, large medical expenses. There are a bunch of other, rarer cases too.

Actually there's a very clear definition of what types of problems ML ought be used for, and that category of problem is what defines it. Those familiar with regression (and stats in general) ought to be familiar with it already - it's an issue of relationship of datatype between independent and dependent variable.

In brief, you're going to run up against two types of data - categorical and continuous. (There are facets to this, eg ordinal, but these are really the elemental types of data). The relationship of datatype to independent/dependent variable is what determines what kind of analysis you may conduct.

Categorical Independent vs. Categorical Dependent, for example, is fairly restrictive, as makes logical sense. You may cross-tabulate, you may score likelihood based on previous observation, but obviously, because all of the data involved are non-numeric, there's no chance for regression, ANOVA, etc. Linear Regression is used when both independent and dependent variables are continuous, and cross-category differencing techniques like ANOVA may be used when the independent is categorical and the dependent is continuous.

The part you don't typically learn until grad school is when the independent is continuous and the dependent is categorical, ie, in ML, a classification problem. The standard statistical methods used as foundation for these problems are logistic regression, logit/probit. It's expansion of these methods that lead to ML in the first place.

I'm not extremely experienced, but it's hard not to agree here. I work with neural networks primarily, but I've had to start learning working with JS for interactive visualization. In short, compared to neural networks, it's a giant nightmare.