7 Ergebnisse.

Learn to expertly apply a range of machine learning methods to real data with this practical guide.This beginner-friendly book teaches machine learning while avoiding complex math. Using the R programming language, it contains few equations, but is packed with real dataset examples and sophisticated advice on how to avoid common pitfalls. You'll review a range of machine learning methods, from the simple to the more complex. You'll encounter d...

From Linear Models to Machine Learning. This text provides a modern introduction to regression and classification with an emphasis on big data and R. The main body uses math stat sparingly and always in the context of something concrete readers can skip the math stat content entirely if they wish. The extras sectionnbspis for those who feel comfortable with analysis using math stat.

This text is designed for a one-semester junior/senior/graduate-level calculus-based course on probability and statistics, aimed specifically at data science students (including computer science). In addition to calculus, the text assumes basic knowledge of matrix algebra and rudimentary computer programming.

This text is designed for a one-semester junior/senior/graduate-level calculus-based course on probability and statistics, aimed specifically at data science students (including computer science). In addition to calculus, the text assumes basic knowledge of matrix algebra and rudimentary computer programming.

This text provides a modern introduction to regression and classification with an emphasis on big data and R. The main body uses math stat sparingly and always in the context of something concrete, readers can skip the math stat content entirely if they wish.

This is one of the first parallel computing books to focus exclusively on parallel data structures, algorithms, software tools, and applications in data science. The book prepares readers to write effective parallel code in various languages and learn more about different R packages and other tools. It covers the classic "n observations, p variables" matrix format and common data structures. Many examples illustrate the range of issues encount...

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like...