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Item #:
15783982
Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
85% of respondents would recommend this to a friend
Item #:
15783982
SAR 139
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What Stands Out
Expert Techniques
This edition offers advanced methodologies and practical insights, equipping readers with cutting-edge skills for building predictive models to enhance decision-making and problem-solving in various industries.
Updated Content
Includes the latest advancements in R programming and machine learning, ensuring readers learn modern techniques that keep pace with the evolving landscape of data science.
Hands-On Learning
Features practical examples and case studies, allowing users to apply learned concepts in real-world scenarios, fostering a deep understanding of machine learning applications.
Product Details
| 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) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for beginners looking to build a solid foundation in machine learning concepts and R programming.
-
Experienced Analysts
Helpful for analysts wanting to deepen their skills in predictive modeling using advanced techniques in R.
-
Researchers
Beneficial for academics requiring practical guidance on implementing machine learning algorithms for data analysis.
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Total Beginners
Would not suit individuals with no prior knowledge of programming or machine learning concepts.
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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