Well you'll certainly need to learn about the math behind these eventually, but you can get started and go quite far simply as a user of a ML library. You can use "systems thinking" and think of ML components as black boxes that do something for you.
A good place to start would be the scikit learn tutorials on youtube: https://www.youtube.com/watch?v=r4bRUvvlaBw I remember going through them and thinking---wow, this covers everything from my ML class in just a few iPython notebooks...
I recommend you start with the basics (datasets, features, training/test/validation data splits) and don't worry too much about the actual choice of model---there will always be shiny new models with better performance but sometimes using the "old stuff" is good enough.
Once you get past the basics want to learn the theory, you can take an online course or find a good book, e.g. https://www.cs.ubc.ca/~murphyk/MLbook/ (advanced, but very comprehensive).
> I recommend you start with the basics (datasets, features, training/test/validation data splits) and don't worry too much about the actual choice of model---there will always be shiny new models with better performance but sometimes using the "old stuff" is good enough.
Or, conversely, it may be that all models are just as bad. This seems to be the case in my domain (formal proofs), where the bottleneck seems to be data representation; it doesn't matter which learning algorithm you use, when your feature selection has stripped out all of the learnable information ;)
A good place to start would be the scikit learn tutorials on youtube: https://www.youtube.com/watch?v=r4bRUvvlaBw I remember going through them and thinking---wow, this covers everything from my ML class in just a few iPython notebooks...
I recommend you start with the basics (datasets, features, training/test/validation data splits) and don't worry too much about the actual choice of model---there will always be shiny new models with better performance but sometimes using the "old stuff" is good enough.
Once you get past the basics want to learn the theory, you can take an online course or find a good book, e.g. https://www.cs.ubc.ca/~murphyk/MLbook/ (advanced, but very comprehensive).