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Mastering Probability and Statistics for Data Science: A Comprehensive Guide with Python
$18.43
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- Introduction to Probability for Computing provides an accessible and thorough overview of probability theory for computer scientists.
- It covers fundamental concepts such as probability spaces, random variables, and conditional probability.
- The book also explores more advanced topics such as Bayesian inference and Markov chains.
- With numerous examples and exercises, Introduction to Probability for Computing is an ideal resource for students and professionals in computer science.
- The book is written in a clear and concise style, making it easy to understand even for those with no prior knowledge of probability theory.
- It is also up-to-date with the latest developments in the field, making it an essential resource for anyone who wants to learn about probability theory for computing.
- The book is self-contained, meaning that it does not require any prior knowledge of mathematics beyond basic algebra.
- It is also well-organized, with each chapter building on the previous one, making it easy to follow the material.
Condition: New original from publisher
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