i do not have any ML experience. Should I watch these ML lectures or do DL1 & 2. Is this ML course complete? like are all the materials on the jupyter notebook or is it in a state of progress? I saw there are only 5 notebooks in the ML course repository.
Also, i have almost completed the first lecture. should i be knowing all the details of scikit,pandas etc.?
I have done a bit of matplotlib. My problem with libraries is that I keep forgetting the module/library specific commands bcoz there are so many of them. Similarly, in the lecture, there are a lot of attributes,dot notations, but how does one remember all that. Documentation is there but how will I use features if I don’t know/remember them at the right time?
Also, after watching 1 lecture how much time should I dedicate to go through the notebook/kaggle datasets before watching other lectures?
I am thinking of going through this MOOC to gain better understanding of pandas,scikit ,data cleaning etc. (https://courses.edx.org/courses/course-v1:UCSanDiegoX+DSE200x+1T2018/course/)
Is that needed or should I just go through the notebook?