Real-World Machine Learning
Real-World Machine Learning
Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods.
KEY FEATURES
Accessible and practical introduction to machine learning
Contains big-picture ideas and real-world examples
Prepares reader to build and deploy powerful predictive systems
Offers tips & tricks and highlights common pitfalls
AUDIENCECode examples are in Python and R. No prior machine learning experience required.
ABOUT THE TECHNOLOGYMachine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all
PRP: 339.92 Lei
Acesta este Pretul Recomandat de Producator. Pretul de vanzare al produsului este afisat mai jos.
305.93Lei
305.93Lei
339.92 LeiLivrare in 2-4 saptamani
Descrierea produsului
Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods.
KEY FEATURES
Accessible and practical introduction to machine learning
Contains big-picture ideas and real-world examples
Prepares reader to build and deploy powerful predictive systems
Offers tips & tricks and highlights common pitfalls
AUDIENCECode examples are in Python and R. No prior machine learning experience required.
ABOUT THE TECHNOLOGYMachine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all
Detaliile produsului