tell me how much learning probability and statistics is necessary for data science machine learnung
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Not much to get started, just the basics of statistical distributions and simple probability.
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So it depends a bit on your exact area, but in addition to the previous feedback I would also add the following fundamental concepts to be relevant:
- hypothesis testing (e.g. to understand false positives / negatives)
- correlation and covariance
- residual analysis, see also: How to evaluate accuracy of a regression model - #21 by Christian_Simonis
- cross validation, see also: Comparaison between data - #2 by Christian_Simonis
- confidence intervals / uncertainty, see also: To Regression or To Classify - #2 by Christian_Simonis
- and statistics fundamentals, e.g. stochastic experiments etc.
That being said: I like to concept of learning by doing and practicing the learned knowledge with real world examples;
Did you check out this specialization? Machine Learning Specialization - DeepLearning.AI
Many of the fundamental concepts are covered in this specialization.
Best regards
Christian
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