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Books > Computing & IT > Applications of computing > Artificial intelligence
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Automated Machine Learning for Business (Hardcover)
Loot Price: R3,085
Discovery Miles 30 850
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Automated Machine Learning for Business (Hardcover)
Expected to ship within 12 - 19 working days
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Teaches the machine learning process for business students and
professionals using automated machine learning, a new development
in data science that requires only a few weeks to learn instead of
years of training Though the concept of computers learning to solve
a problem may still conjure thoughts of futuristic artificial
intelligence, the reality is that machine learning algorithms now
exist within most major software, including Websites and even word
processors. These algorithms are transforming society in the most
radical way since the Industrial Revolution, primarily through
automating tasks such as deciding which users to advertise to,
which machines are likely to break down, and which stock to buy and
sell. While this work no longer always requires advanced technical
expertise, it is crucial that practitioners and students alike
understand the world of machine learning. In this book, Kai R.
Larsen and Daniel S. Becker teach the machine learning process
using a new development in data science: automated machine learning
(AutoML). AutoML, when implemented properly, makes machine learning
accessible by removing the need for years of experience in the most
arcane aspects of data science, such as math, statistics, and
computer science. Larsen and Becker demonstrate how anyone trained
in the use of AutoML can use it to test their ideas and support the
quality of those ideas during presentations to management and
stakeholder groups. Because the requisite investment is a few weeks
rather than a few years of training, these tools will likely become
a core component of undergraduate and graduate programs alike. With
first-hand examples from the industry-leading DataRobot platform,
Automated Machine Learning for Business provides a clear overview
of the process and engages with essential tools for the future of
data science.
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