What's the best way for college freshmen to learn about ML? -- A.I. and ML aren't really topics talked about until upper-divs, which means a year or two out for me.
You can certainly learn to implement the APIs that are available, but in terms of really understanding I'd say wait a bit.
Take classes in probability, and linear algebra, from there you'll begin to have the level of mathematics to be able to really dive in. You'll also have the computer science maturity to better understand the libraries in use, and in truth the field will have advanced a bit in the time it takes for you to get those foundations.
There was just a good reddit[1] thread about what topics to focus on in linear algebra and probability that you should be paying attention to, because those two subjects are largely the mathematical foundations of machine learning.
These 3 are the most well know and well regarded 0-to-hero type intro courses online, and high-school math is sufficient to follow along (but pick only one and go start to finish!):
* https://www.udacity.com/course/intro-to-artificial-intellige... (by Peter Norvig - director of research @ Google & Sebastian Thrun - lead dev of google self driving car and founder of google x, now at gerogia tech uni) - great if you want a more "deep thinking" style intro to AI
* https://www.udacity.com/course/intro-to-machine-learning--ud... (Sebastian Thrun & Katie Malone - former physicist and data scientist great at explaining stuff so that anyone can grok it) - great if you want a more "down to earth" engineering style intro with simple clear examples
* https://www.coursera.org/learn/machine-learning (Andrew Ng @ Stanford & chied scientist at Baidu, former Google researcher) - great if you want a "bottom up", from math, through code/engineering, with less fuzzy big picture stuff - this is a great intro, even if Andrew Ng is less of a rock-star-presenter, if you want to start from math details up take this one!
Oh, and kaggle: https://www.kaggle.com/ . If you get stuck on anything, google the relevant math, pick up just enough to have an intuition and carry on.
You're still in college so you have plenty of time to learn well the required math, it's better to get a broad picture of the field ASAP imho! Then when you'll take the math classes, you'll already have "aha, this feels my gap about X and Y" and "aha, now I get why Z" and you'll really love that math after you already know what problems it solves!
(PS if you're less of a "highly logico-intuitive" person and more "analytical rigorous thinker" instead, just ignore my last paragraph and focus on the math, but try to get some deep intuition of probability along the way)
Take classes in probability, and linear algebra, from there you'll begin to have the level of mathematics to be able to really dive in. You'll also have the computer science maturity to better understand the libraries in use, and in truth the field will have advanced a bit in the time it takes for you to get those foundations.
There was just a good reddit[1] thread about what topics to focus on in linear algebra and probability that you should be paying attention to, because those two subjects are largely the mathematical foundations of machine learning.
[1]:https://www.reddit.com/r/MachineLearning/comments/5klywi/d_w...