Электронные книги

Жанры
Реклама
Последние комментарии
От партнёров
Облако тегов

ПрограммированиеMachine Learning in Action

Machine Learning in ActionНазвание: Machine Learning in Action
Автор: Peter Harrington
Издательство: Manning Publications
ISBN: 1617290181
Год: 2012
Страниц: 384
Формат: PDF
Размер: 8 Mb
Язык: Английский

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action
is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.
What's Inside

A no-nonsense introduction
Examples showing common ML tasks
Everyday data analysis
Implementing classic algorithms like Apriori and Adaboos

Нажмите для скачивания 1617290181MachLearn.pdf!1617290181MachLearn.pdf
Размер: 8.51 Mb(cкачиваний: 0)



Похожие книги

Информация

Посетители, находящиеся в группе Гости, не могут оставлять комментарии к данной публикации.


  • Valid XHTML 1.0 Transitional