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15783982
Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
85% of respondents would recommend this to a friend
Item #:
15783982
€ 34
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Product Details
- Third edition of the bestselling R machine learning book, updated for R 3.6 and beyond
- Learn to build flexible and transparent machine learning models with R
- Clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz
- Explore ethical and bias issues, and an introduction to deep learning with R 3.6
- Apply machine learning to real-world problems, uncover insights, make predictions, and visualize findings
- Discover origins of machine learning, prepare data, classify outcomes, predict future events, and model complex processes with R
| Publisher | Packt Publishing |
| Publication date | April 15, 2019 |
| Edition | 3rd ed. |
| Language | English |
| Print length | 458 pages |
| ISBN-10 | 1788295862 |
| ISBN-13 | 978-1788295864 |
| Item Weight | 1.89 pounds (860 grams) |
| Dimensions | 7.5 x 1.04 x 9.25 inches (19.1 x 2.6 x 23.5 cm) |
Product Description
Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
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Intelligence & Semantics Editorial Review
Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition receives mixed reviews from customers. Some reviewers find the book difficult to follow, mentioning that the examples are overly simplified and misleading. They also criticize the code, stating that it often doesn't work and is inefficiently written. Additionally, the lack of an index for functions and a clear organizational structure is deemed problematic by some readers. It is worth noting that there are users who appreciate the book, particularly those with prior experience in R who find the projects in the book to be beneficial. However, there is disappointment expressed by users who were expecting updated content compared to the previous edition.
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Pros
- Provides good practice with projects for those with prior R experience
- Well-written for beginners
- Offers introductory content on machine learning
Cons
- Examples are unrealistic and misleading
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Features & Benefits
- Solve real-world data problems with R and machine learning
- Updated and improved for R 3.6 and beyond
- Build flexible, effective, and transparent machine learning models
- Hands-on guide by experienced machine learning teacher and practitioner
- Uncover key insights, make new predictions, and visualize findings
- Learn the origins of machine learning and understand how computers learn
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