Practical Machine Learning in R
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Practical Machine Learning in R
US $10.97
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Condition:
Good
A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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Located in: Annandale, New Jersey, United States
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Estimated between Wed, 24 Sep and Mon, 29 Sep to 94104
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eBay item number:326430891199
Item specifics
- Condition
- ISBN
- 9781119591511
About this product
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
1119591511
ISBN-13
9781119591511
eBay Product ID (ePID)
22038790714
Product Key Features
Number of Pages
464 Pages
Language
English
Publication Name
Practical Machine Learning in R
Subject
Software Development & Engineering / General, Intelligence (Ai) & Semantics, Databases / Data Mining
Publication Year
2020
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Height
0.8 in
Item Weight
32.1 Oz
Item Length
9.1 in
Item Width
7.3 in
Additional Product Features
Intended Audience
Scholarly & Professional
Dewey Edition
23
Dewey Decimal
006.31
Synopsis
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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