Practical Machine Learning with R and Python: Third Edition: Machine Learning in Stereo
Practical Machine Learning with R and Python: Third Edition: Machine Learning in Stereo
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Practical Machine Learning with R and Python: Third Edition: Machine Learning in Stereo
Practical Machine Learning with R and Python: Third Edition: Machine Learning in Stereo
This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python.
Practical Machine Learning with R and Python: Third Edition: Machine Learning in Stereo
Nº de artículo: 212878414

Practical Machine Learning with R and Python: Third Edition: Machine Learning in Stereo

87% of respondents would recommend this to a friend

Nº de artículo: 212878414

CRC 11467

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This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python.
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What Stands Out

Dual Language Coverage
Integrates practical applications in both R and Python, enhancing learning for users familiar with either programming language, catering to a broader range of data science professionals.
Hands-On Approach
Focuses on real-world machine learning applications, ensuring learners can implement concepts effectively, bridging the gap between theoretical knowledge and practical execution.
Updated Content
Includes the latest advancements and techniques in machine learning, providing readers with current best practices and tools that enhance their skills and keep them relevant in a rapidly evolving field.

Detalles de producto

Shop Practical Machine Learning with R and Python: Third Edition: Machine Learning in Stereo online at a best price in Costa Rica. 1792969309
  • This is the 3rd edition of the book. All the code sections are formatted with fixed-width font Consolas for better readability. This book implements many common Machine Learning algorithms in equivalent R and Python. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques like best-fit, forward fit, backward fit, and lasso and ridge regression are also covered. The book further touches on classification metrics for computing accuracy, recall, precision etc. There are implementations of validation, ROC and AUC curves in both R and Python. Finally, the book covers unsupervised learning methods like K-Means, PCA and Hierarchical clustering.The book is well suited for the novice and the expert. The first two chapters discuss the most important programming constructs in R and Python. The third chapter highlights equivalent programming phrases in R and Python. Hence, those with no knowledge of R and Python will find these introductory chapters useful. Those who are proficient in one of the language can further their knowledge on the other. Those are familiar with both R and Python will find the equivalent implementations useful to internalize the algorithms. This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python
Publisher Independently published
Publication date January 1, 2019
Language English
Print length 276 pages
ISBN-10 1792969309
ISBN-13 978-1792969300
Item Weight 1.1 pounds (500 grams)
Dimensions 7.44 x 0.63 x 9.69 inches (18.9 x 1.6 x 24.6 cm)

Who Should Buy?

Suitable For
  • Beginner Data Scientists

    Ideal for newcomers to machine learning, providing foundational knowledge using both R and Python programming.

  • Academic Students

    Great for students in data science courses who need practical examples and exercises in their curriculum.

  • Practical Analysts

    Suitable for analysts looking to implement machine learning models in real-world applications using popular programming languages.

Not Suitable For
  • Advanced Practitioners

    Experienced data scientists may find the content too basic and not sufficiently challenging or advanced.

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Preguntas y respuestas de los clientes

  • Pregunta: What is the focus of 'Practical Machine Learning With R And Python: Third Edition'?

    Respuesta: This book focuses on providing a comprehensive understanding of machine learning using R and Python together. It covers essential concepts, techniques, and real-world applications, making it suitable for both beginners and advanced users. By utilizing both programming languages, readers gain insights into various machine learning algorithms, data processing techniques, and practical implementation strategies. Practical use cases include predictive analytics in business, natural language processing for text analysis, and image classification projects.
  • Pregunta: Who is the target audience for this book?

    Respuesta: The target audience for this book includes data scientists, machine learning practitioners, and students interested in enhancing their skills in machine learning. It’s suitable for readers who are familiar with the basics of programming and statistical analysis, allowing them to transition into more advanced topics seamlessly. The book also serves as a valuable reference for professionals looking to implement machine learning techniques in their work across various industries, including finance, healthcare, and technology.
  • Pregunta: What programming skills are required to understand the content of this book?

    Respuesta: A basic understanding of programming in R and Python is recommended to get the most out of this book. Familiarity with data manipulation, basic statistics, and machine learning concepts will greatly enhance your learning experience. The book provides clear explanations and examples, so even if you're not an expert, you can gradually build your skills. This approach is useful for applying machine learning in various scenarios, such as developing recommendation systems or conducting data-driven research.
  • Pregunta: What kind of practical examples does the book provide?

    Respuesta: The book offers multiple practical examples that demonstrate the application of various machine learning algorithms using R and Python. Examples include creating predictive models for sales forecasting, employing clustering methods to segment customers, and using decision trees for classification tasks. Each example is designed to be relatable, providing context that readers can apply directly to their own projects, thus bridging theory and practice.
  • Pregunta: How does this edition differ from previous editions?

    Respuesta: The third edition of 'Practical Machine Learning With R And Python' has been updated to include the latest advancements in the field of machine learning and incorporates new libraries and frameworks. It also provides enhanced examples and case studies relevant to contemporary issues, making it more applicable to today's data science challenges. The inclusion of updated tools helps practitioners stay current with the rapidly evolving machine learning landscape, making it an essential resource.
  • Pregunta: Are there prerequisites to keep in mind before reading this book?

    Respuesta: While there are no formal prerequisites, readers are encouraged to have a foundational knowledge of programming concepts and basic statistics. Familiarity with data manipulation and analysis will greatly benefit those looking to dive into machine learning with R and Python. For those who are complete beginners, it might be helpful to take introductory courses or tutorials in both programming languages prior to beginning the book, ensuring a smoother learning experience.
  • Pregunta: What types of projects can I implement after learning from this book?

    Respuesta: After studying the content in this book, you can implement various projects such as developing a movie recommendation system, predicting customer churn in subscription services, or analyzing social media sentiment. The skills acquired allow you to tackle real-world data science problems across disciplines, enabling innovative solutions that can help organizations make data-informed decisions. This practical approach is essential for anyone looking to apply machine learning techniques effectively.
  • Pregunta: Is this book suitable for advanced machine learning practitioners?

    Respuesta: Yes, this book caters not only to beginners but also to advanced machine learning practitioners. It delves into complex topics and advanced techniques, offering insights into performance optimization and model evaluation. For seasoned data scientists, it serves as a valuable reference for frameworks and hands-on implementations that may not be covered extensively in typical coursework. This broadens their toolkit and fosters new ideas for advanced projects or research.
  • Pregunta: What resources does the book recommend for further learning?

    Respuesta: The book provides a variety of resources for further learning, including online courses, tutorials, and specific library documentation for R and Python. It also suggests various datasets for practical experimentation, enabling continued practice and exploration beyond the book's exercises. Engaging with these additional resources can significantly enhance your understanding and skills in machine learning, supporting your development as a proficient data scientist.
  • Pregunta: Where can I buy 'Practical Machine Learning With R And Python: Third Edition' in Costa Rica?

    Respuesta: You can buy 'Practical Machine Learning With R And Python: Third Edition' at Ubuy. Ubuy offers a wide selection of books and provides international shipping options. Their user-friendly interface makes it easy to find and purchase this title, ensuring you can get your hands on it quickly and efficiently, giving you the tools to dive into machine learning.

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CRC 11467

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características y beneficios

  • Third edition offers updated content and code.
  • Covers a wide range of Machine Learning algorithms.
  • Provides R and Python implementations side-by-side.
  • Includes essential programming concepts for beginners.
  • Serves as a handy reference for both novices and experts.
  • Focuses on practical applications with detailed examples.

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