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Taylor & Francis Ltd Paperback English

Multivariate Statistics and Machine Learning

An Introduction to Applied Data Science Using R and Python

By Daniel J. Denis

Regular price £56.99
Unit price
per

Taylor & Francis Ltd Paperback English

Multivariate Statistics and Machine Learning

An Introduction to Applied Data Science Using R and Python

By Daniel J. Denis

Regular price £56.99
Unit price
per
 
Dispatched Monday, 22nd June with FREE Express Tracked Delivery
Delivery expected between Tuesday, 23rd June and Wednesday, 24th June
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  • Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software. The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued. With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical and computational sciences for years to come.
Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software. The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued. With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical and computational sciences for years to come.