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Practical Machine Learning with R: Define, build, and evaluate machine learning models for real-world applications
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CLP 14213
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Gain a comprehensive overview of different machine learning techniques
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What Stands Out
Detalles de producto
- Gain a comprehensive overview of different machine learning techniques
- Explore various methods for selecting a particular algorithm
- Implement a machine learning project from problem definition through to the final model
- Learn to define, verify, and clean data before transforming it for use
- Build models for neural net, linear and non-linear regression, classification, and clustering
- Suitable for data analysts, scientists, business analysts, and novice programmers with basic programming knowledge
| Publisher | Packt Publishing |
| Publication date | August 30, 2019 |
| Language | English |
| Print length | 416 pages |
| ISBN-10 | 1838550135 |
| ISBN-13 | 978-1838550134 |
| Item Weight | 1.57 pounds (710 grams) |
| Dimensions | 7.5 x 0.94 x 9.25 inches (19.1 x 2.4 x 23.5 cm) |
Who Should Buy?
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Aspiring Data Scientists
Ideal for those starting their journey in data science with practical insights into machine learning using R.
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Business Analysts
Perfect for analysts who want to leverage machine learning for data-driven decision-making in their organizations.
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Students in Academia
Beneficial for students learning machine learning concepts and wanting hands-on experience through R programming.
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Complete Beginners
Not suitable for individuals without any background in programming or data analysis, as it assumes prior knowledge.
DESCRIPCIÓN DEL PRODUCTO
Practical Machine Learning with R: Define, build, and evaluate machine learning models for real-world applications
About This Item
Introducing "Practical Machine Learning with R: Define, build, and evaluate machine learning models for real-world applications" - the ultimate guide to mastering the power of machine learning with R. Whether you're a data scientist, a student, or a professional looking to venture into the world of machine learning, this book is your gateway to success. Authored by Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, and Monicah Wambugu - industry experts with years of experience in data science and machine learning - this book unravels the complexities of machine learning and provides you with practical insights and techniques to tackle real-world business problems. R for Machine Learning: With R being one of the most popular languages for data science and machine learning, this book shows you how to harness the power of R to build and deploy machine learning models. Supervised and Unsupervised Machine Learning: Explore the fundamental concepts of both supervised and unsupervised machine learning, and understand how they can be applied to solve various types of problems. Classification Algorithms and Regression Analysis: Learn about different classification algorithms used in machine learning and how to perform accurate regression analysis. Data Preprocessing and Feature Selection: Discover the importance of data preprocessing and feature selection techniques in machine learning, and how they impact model performance. Evaluation Metrics and Model Comparison: Understand how to evaluate and compare machine learning models using various metrics, and choose the best model for your specific application. Deep Learning, NLP, and Computer Vision: Dive into the exciting domains of deep learning, natural language processing (NLP), and computer vision, and learn how to apply them in practical scenarios. Real-World Applications: Gain insights into the application of machine learning in predictive analytics, fraud detection, customer segmentation, sentiment analysis, image recognition, and more. With its comprehensive coverage of essential machine learning topics and its focus on real-world applications, "Practical Machine Learning with R" is your go-to resource for mastering the art of machine learning. Start your journey towards becoming a machine learning expert today!.
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ventajas
- Clear and practical examples
- Engaging writing style
- Well-structured content
- Helpful for beginners
Contras
- Some chapters could be more detailed.
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CLP 14213
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características y beneficios
- Practical Machine Learning with R
- Defines, builds, and evaluates machine learning models
- Explore various methods for selecting a particular algorithm
- Implement a machine learning project from problem definition through to the final model
- Gain hands-on experience of building a machine learning solution in R
- Learn different machine learning techniques for both supervised and unsupervised learning approaches
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