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A comment on Machine Learning 101: An Intro to Utilizing Decision Trees. Parent comment
First, learn and understand the central limit theorem. That will force you to understand enough statistics to not be dangerous.
Then I'd work through https://www.kaggle.com/omarelgabry/titanic/a-journey-through... until I can do the whole thing myself.
Then I'd do a Kaggle. I like Kaggle because the datasets are well prepared, and the problems are well stated. There are plenty of other similar datasets etc if you don't want to do that.
I don't know any specific Python books though.
[1] https://www.khanacademy.org/math/statistics-probability/samp... maybe? I haven't watched these, but generally the Khan Academy stuff is a good place to start.
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First, learn and understand the central limit theorem. That will force you to understand enough statistics to not be dangerous.
Then I'd work through https://www.kaggle.com/omarelgabry/titanic/a-journey-through... until I can do the whole thing myself.
Then I'd do a Kaggle. I like Kaggle because the datasets are well prepared, and the problems are well stated. There are plenty of other similar datasets etc if you don't want to do that.
I don't know any specific Python books though.
[1] https://www.khanacademy.org/math/statistics-probability/samp... maybe? I haven't watched these, but generally the Khan Academy stuff is a good place to start.