Machine learning is all about predictions, supervised learning, unsupervised learning, etc.
Statistics is about sample, population, hypothesis, etc.
然後 Astash Shah 說統計是數學的分枝科目，而機器學習的理論技術則是源自人工智慧。
Machine learning is a subfield of computer science and artificial intelligence. It deals with building systems that can learn from data, instead of explicitly programmed instructions.
A statistical model, on the other hand, is a subfield of mathematics.
ML professional: “The model is 85% accurate in predicting Y, given a, b and c.”
Statistician: “The model is 85% accurate in predicting Y, given a, b and c; and I am 90% certain that you will obtain the same result.”
The difference between the two has reduced significantly over the past decade. Both the branches have learned from each other a lot and will continue to come closer together in the future.
But, understanding the association and knowing their differences enables machine learners and statisticians to expand their knowledge and even apply methods outside their domain of expertise. This is the notion of “data science” itself, which aims to bridge the gap.