
Sách Machine Learning Using R With Time Series and Industry-Based Use Cases in R (sách keo gáy, bìa mềm)
Categories:Computers - Computer Science
Year:2019
Language:english
Pages:712
Examine the latest technological advancements in building a scalable
machine-learning model with big data using R. This second edition shows
you how to work with a machine-learning algorithm and use it to build a
ML model from raw data. You will see how to use R programming with
TensorFlow, thus avoiding the effort of learning Python if you are only
comfortable with R.
As in the first edition, the authors have kept
the fine balance of theory and application of machine learning through
various real-world use-cases which gives you a comprehensive collection
of topics in machine learning. New chapters in this edition cover time
series models and deep learning.
What You'll Learn
Understand machine learning algorithms using R
Master the process of building machine-learning models
Cover the theoretical foundations of machine-learning algorithms
See industry focused real-world use cases
Tackle time series modeling in R
Apply deep learning using Keras and TensorFlow in R
Who This Book is For
Data
scientists, data science professionals, and researchers in academia who
want to understand the nuances of machine-learning
approaches/algorithms in practice using R.
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