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This one is on your request,
Best statistic books for data science:
Practical statistics for data scientists
by Peter Bruce and Andrew Bruce
https://math2510.coltongrainger.com/books/2017-bruce-and-bruce-pratical-statistics-for-data-scientists.pdf
Think Stats
by Allen B. Downey
http://greenteapress.com/thinkstats/thinkstats.pdf
Computer Age Statistical Inference
by Bradley Efron and Trevor Hastie
https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf
Statistics in Plain English
by Timothy C. Urdan
https://www.book2look.com/embed/9781317526988
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Best statistic books for data science:
Practical statistics for data scientists
by Peter Bruce and Andrew Bruce
https://math2510.coltongrainger.com/books/2017-bruce-and-bruce-pratical-statistics-for-data-scientists.pdf
Think Stats
by Allen B. Downey
http://greenteapress.com/thinkstats/thinkstats.pdf
Computer Age Statistical Inference
by Bradley Efron and Trevor Hastie
https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf
Statistics in Plain English
by Timothy C. Urdan
https://www.book2look.com/embed/9781317526988
Join @realgroupforprogrammer for more
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MIT machine learning course. Duration: 13 weeks.
Published: December 2020.
Type: open-source.
https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/about
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Published: December 2020.
Type: open-source.
https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/about
Join @realgroupforprogrammer for more
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openlearninglibrary.mit.edu
Introduction to Machine Learning
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These conceptsβ¦