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Practical Machine Learning in R (Paperback)
Loot Price: R729
Discovery Miles 7 290
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Practical Machine Learning in R (Paperback)
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Total price: R749
Discovery Miles: 7 490
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Guides professionals and students through the rapidly growing field
of machine learning with hands-on examples in the popular R
programming language Machine learning--a branch of Artificial
Intelligence (AI) which enables computers to improve their results
and learn new approaches without explicit instructions--allows
organizations to reveal patterns in their data and incorporate
predictive analytics into their decision-making process. Practical
Machine Learning in R provides a hands-on approach to solving
business problems with intelligent, self-learning computer
algorithms. Bestselling author and data analytics experts Fred
Nwanganga and Mike Chapple explain what machine learning is,
demonstrate its organizational benefits, and provide hands-on
examples created in the R programming language. A perfect guide for
professional self-taught learners or students in an introductory
machine learning course, this reader-friendly book illustrates the
numerous real-world business uses of machine learning approaches.
Clear and detailed chapters cover data wrangling, R programming
with the popular RStudio tool, classification and regression
techniques, performance evaluation, and more. Explores data
management techniques, including data collection, exploration and
dimensionality reduction Covers unsupervised learning, where
readers identify and summarize patterns using approaches such as
apriori, eclat and clustering Describes the principles behind the
Nearest Neighbor, Decision Tree and Naive Bayes classification
techniques Explains how to evaluate and choose the right model, as
well as how to improve model performance using ensemble methods
such as Random Forest and XGBoost Practical Machine Learning in R
is a must-have guide for business analysts, data scientists, and
other professionals interested in leveraging the power of AI to
solve business problems, as well as students and independent
learners seeking to enter the field.
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