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| Der Titel des Buches | Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition |
| Veröffentlichungsdatum | |
| Sprache | Deutsch |
| ISBN-10 | 1681734878-TGV |
| Digital ISBN | 640-0314725849-NGY |
| Autor | Rebekka Sauerbrunn |
| Übersetzer | Udonna Faakhir |
| Seitenzahl | 103 Pages |
| Editor | Vanessa Henze |
| Datentyp | EPub PDF AMZ HWP WRD |
| Dateigröße | 4.20 MB |
| Dateinamen | Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition.pdf |
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Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book with newer and better ...
How To Get Started With Machine Learning Algorithms in R: Links to a large number of code examples on this site demonstrating machine learning algorithms in R. Machine Learning Algorithm Recipes in scikit-learn: A collection of Python code examples demonstrating how to create predictive models using scikit-learn.
The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of ...
Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition. Brett Lantz . 4.6 out of 5 stars 141. Paperback. $38.99 R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Hadley Wickham. 4.7 out of 5 stars 907. Paperback. $41.14 An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) Gareth James. 4.7 out of 5 stars ...
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get ...
The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is ...
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Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical n updating is particularly important in the dynamic analysis of a sequence of data.