I've taken an ML course previously, but now that I am working with ML related projects at my job, I am struggling quite a bit to actually apply it. I'm sure the stuff I'm doing has been researched/dealt with before, but I can't find specific topics.
All the machine learning examples I find online are very simple (e.g. how to use a KMeans model in Python and look at the predictions). I am looking for good resources on how to actually apply these, and maybe code examples of large scale machine learning implementations and model trainings. I want to learn about how to effectively process and create new data that can make the ML algorithms much more effective.
Best Answer
I do not have knowledge in ML. After a little web searching, I found a reddit thread that lists the following books - all of which are legally downloadable for free. You can research the titles of your interest for details. Also comment if you find any of the books helpful (and why).
Machine Learning
Probability / Stats
Linear Algebra / Optimization
Genetic Algorithm