From own experience (switched to ML 1.5 years ago):<p>1. That software engineering skills are way more important than ML skills.<p>2. That you'd be spending more time on making presentation than doing ML (and it makes sense, it's very important to present statistics properly).<p>3. That most problems don't need good ML models. Something cheap and easy is often good enough. What you do need to be good, is data pipelines around them (see 1.)<p>In my case, I learned ML enough to feel "senior" compared to other people in company and online in less than a year. Same path to Senior SWE took me much longer (way larger mandatory knowledge base, probably because ML is a young field). So I'd say ML is definitely easier.
It's starting to become a cliche (which might be a good thing), but building datasets, cleaning that data and validating that data is the hard part..by far. The actual machine learning is quickly become a commodity.
Except in rare cases, or specific teams tackling problems that are both exceptionally hard, and exceptionally well-suited for deep learning, I would take someone with some medium value stats and advanced python/pandas coding ability over a PhD in ML.
Sometimes people you work with, like team members or PMs, will really want to understand ML and be involved but will have a hard time grasping the concepts being discussed. I found it really helped to draw out and illustrate the different components and data flows!
The best places to start for a complete beginner are Precalculus and Hello-World in C.<p>I'm serious about this. Ultimately the job is just software development plus statistics.<p>If you are a software developer, work on your statistics.<p>If you're a statistician, learn to program.<p>Most people will have gaps in both of these sub-fields.<p>Do not, under any circumstances, take any online courses that include the phrases "data science" or "machine learning" in the title.
Your ability to develop an amazing ML model is limited by your organization's ability to collect and clean data. However, the great news is that most problems do not need an incredible model. Small uplifts in performance could still result in substantial outcomes.<p>In industry, you also need to balance the amount of time and effort it takes to build your model against the incremental benefit.