Physcab suggests a great step #2: diving into some real statistics problems. The information available online today is more than at that point.
Step #1 is going to be getting the foundations so that you can quickly digest the meaning and purpose of things like machine learning. Fortunately, statistics can be largely summarized as "the science/math/art of explaining variance".
Study what variance is and means and you'll dive through probability, distributions, modeling, inference, prediction, parametrization, simulation, and all of those fun topics while keeping an understanding for why they exist.
Finally, I'd highly suggest taking a look at Tufte's work because once you understand variance, you've still got to explain it.
Step #1 is going to be getting the foundations so that you can quickly digest the meaning and purpose of things like machine learning. Fortunately, statistics can be largely summarized as "the science/math/art of explaining variance".
Study what variance is and means and you'll dive through probability, distributions, modeling, inference, prediction, parametrization, simulation, and all of those fun topics while keeping an understanding for why they exist.
Finally, I'd highly suggest taking a look at Tufte's work because once you understand variance, you've still got to explain it.