Learning Machine Learning

I’m currently a mechanical engineering student, and I’m looking into machine learning as a side skill. I am good in python programming at least relating to data science (Made a LMS as CSV files as its database :rofl: . As a mechanical engineer I believe I will be facing many prediction problems, especially regression related. e.g. working on prediction of pollutant concentrations values. I have a few questions. First should I learn the mathematics/ concepts behind machine learning like everything or not? I am fairly good at grasping concepts, especially math related. My style is brute like I only learn that’s going to help me get the job done. Should I limit myself to only regression related learning or not? Lastly Should I limit to only ML with scikit or learn things like TensorFlow and such as well?

I want your opinion. Second, if possible, recommend me some good courses/ road map as well. Some books as well.
Any kind of help is much appreciated.

i meant math behind it. like for the mentioned project. i just googled different models and then used all and compare results to decide which one will work best :laughing:. all of the scikit models use almost same syntaxes. and i think i mean Properly learning them. like i am good enough with statistics to know how common models work .eg linear regression model is simply a slope function. and Knn uses nearest points to decide. Also sorry for hard to understand English :joy:. oh i forgot to mention i already used one hot encoding for dates in my first model. Later found out it’s actually a technique :joy:

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