I don't like when you provide a list of loads of python libraries to try, I ignore these emails. What I love about your posts is that you provide a single easily digestible tip. Providing a big list of things you could try that would take ages to read through seems to defeat the point.
what I find lacking in all data science blogs that I read concerns the conditions when an analyst begins to take steps outside the objective rules of science and begins to place more faith in the model itself than its general utility at solving a problem. how should analysts deal with the tendency to wish to over fit a model to increase its accuracy? What objective rules can analysts follow to retain negative feedback data in their models rather than adjusting the model to accomodate for outliers?
hi avi, i love the work that you do and as a python beginner, i find your emails informative. one constructive feedback would be to reduce the number of posts and make it once or twice every week but not to compromise on the the quality. I think you may want to look into breaking a topic down into 2-3 parts that makes it relevant to different platforms. shorter version could be a daily drop on linkedin while the emailer could be more elaborate and long form. One reason why i say this is that while I like to emails, the inbox clutter sometimes makes me skip the older ones. all the best and keep it up!
I don't like when you provide a list of loads of python libraries to try, I ignore these emails. What I love about your posts is that you provide a single easily digestible tip. Providing a big list of things you could try that would take ages to read through seems to defeat the point.
what I find lacking in all data science blogs that I read concerns the conditions when an analyst begins to take steps outside the objective rules of science and begins to place more faith in the model itself than its general utility at solving a problem. how should analysts deal with the tendency to wish to over fit a model to increase its accuracy? What objective rules can analysts follow to retain negative feedback data in their models rather than adjusting the model to accomodate for outliers?
hi avi, i love the work that you do and as a python beginner, i find your emails informative. one constructive feedback would be to reduce the number of posts and make it once or twice every week but not to compromise on the the quality. I think you may want to look into breaking a topic down into 2-3 parts that makes it relevant to different platforms. shorter version could be a daily drop on linkedin while the emailer could be more elaborate and long form. One reason why i say this is that while I like to emails, the inbox clutter sometimes makes me skip the older ones. all the best and keep it up!