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20433158
Practical Machine Learning with R: Define, build, and evaluate machine learning models for real-world applications
Item #:
20433158
SAR 51
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What Stands Out
Comprehensive Guidance
Offers a step-by-step approach to defining, building, and evaluating machine learning models, making complex concepts accessible for beginners and professionals seeking real-world applications.
Hands-On Examples
Includes practical examples and projects that enable users to apply what they learn in real-time, bridging the gap between theory and practice effectively.
R Language Focus
Specifically designed for R users, maximizing the power of the language’s libraries for machine learning, ensuring that users leverage its full potential in their applications.
Product Details
- Gain a comprehensive overview of different machine learning techniques
- Explore various methods for selecting a particular algorithm
- Implement machine learning projects from problem definition through to the final model
- Understand how to get machine learning algorithms to work in practice
- Learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression using R packages
- Gain expertise in solving business problems using machine learning in R
| Country of Origin | This item will be imported from US |
| Item Weight | 1.5 lbs (680 grams) |
| What is in the box | Practical Machine Learning with... For more details, please check description/product details |
Who Should Buy?
-
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.
Product Description
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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Customer Questions & Answers
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Question:
What topics are covered in Practical Machine Learning with R?
Answer: Practical Machine Learning with R dives into key concepts such as data preprocessing, exploratory data analysis, predictive modeling, and evaluation techniques. Each chapter presents a blend of theory and practical hands-on examples, allowing readers to apply machine learning algorithms directly using the R programming language. This structured approach is beneficial for beginners wanting to understand the basics of machine learning as well as for experienced practitioners looking to refresh their skills through practical scenarios. -
Question:
Who is the target audience for Practical Machine Learning with R?
Answer: The book is primarily aimed at students, data scientists, and professionals seeking to enhance their skills in machine learning. It's perfect for those who have a foundational understanding of R and want to delve deeper into machine learning applications. The practical examples provided facilitate self-learning, making it suitable for anyone ranging from novices to experienced programmers looking to apply machine learning in real-world contexts. -
Question:
Which machine learning algorithms are implemented in the book?
Answer: Practical Machine Learning with R covers a variety of algorithms such as linear regression, decision trees, random forests, support vector machines, and neural networks. The author demonstrates how to implement these algorithms within R's framework, enabling readers to see their applications in real-time. Through these examples, readers will grasp the nuances of different algorithms, empowering them to select the right one based on the specific challenges they may face in practical scenarios. -
Question:
Is prior knowledge of R programming required?
Answer: While prior knowledge of R programming is not mandatory, a basic understanding of R will enhance your learning experience. The book starts from fundamental concepts and gradually introduces more complex topics, making it accessible for beginners. However, those already familiar with R will find it easier to engage with the practical exercises, ultimately allowing for more efficient skill development in the context of machine learning. -
Question:
Can I find code examples in Practical Machine Learning with R?
Answer: Yes, the book includes numerous code examples that illustrate the application of machine learning techniques using R. These examples demonstrate how to implement algorithms step-by-step, making it easier for readers to follow along and gain hands-on experience. By working through the code, readers can reinforce their understanding and apply the concepts to their own data-related projects effectively. -
Question:
What are some practical applications of machine learning mentioned in the book?
Answer: Practical Machine Learning with R discusses various real-life applications, including fraud detection, customer segmentation, and recommendation systems. These use cases highlight how machine learning can be utilized across different industries such as finance, marketing, and healthcare. By understanding the application of these algorithms, readers can better envision how to implement machine learning solutions in their respective fields. -
Question:
Does the book provide insights into model evaluation?
Answer: Yes, the book includes vital sections on model evaluation techniques. It explains metrics like accuracy, precision, recall, and F1 score that help assess the performance of machine learning models. Understanding these evaluation methods is crucial for anyone looking to validate their algorithms effectively. Working with real datasets in these evaluations enhances the reader's ability to measure the success of their models based on project-specific goals. -
Question:
How does Practical Machine Learning with R compare to similar books?
Answer: Practical Machine Learning with R distinguishes itself through its practical focus and hands-on approach. Unlike other books that may be heavily theoretical, this one emphasizes real-world applications and provides step-by-step coding guidance. This hands-on experience equips readers with the confidence to tackle machine learning projects independently, making it a valuable resource for both classroom settings and self-learners. -
Question:
Are there supplementary resources available for readers?
Answer: Yes, many online resources accompany Practical Machine Learning with R, including repositories for downloadable code and datasets used in the book. Additionally, various online forums and communities are beneficial for engaging with other learners. Participation in these platforms helps to clarify doubts and share experiences, fostering a supportive learning environment while allowing readers to grow their knowledge and skill set. -
Question:
Where can I buy Practical Machine Learning with R in Saudi Arabia?
Answer: You can purchase Practical Machine Learning with R from Ubuy in Saudi Arabia. Ubuy specializes in providing a wide range of books and educational materials, ensuring that you have access to the resources you need to enhance your understanding of machine learning with R. Their user-friendly platform makes it simple to place an order and have it delivered to your location.
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Features & Benefits
- 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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