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Machine learning using R : with time series and industry-based uses in R /
Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[New York, New York] :
Apress,
[2019]
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Ramasubramanian, Karthik, |e author. | |
| 245 | 1 | 0 | |a Machine learning using R : |b with time series and industry-based uses in R / |c Karthik Ramasubramanian, Abhishek Singh. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a [New York, New York] : |b Apress, |c [2019] | |
| 300 | |a 1 online resource (xxiv, 700 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 505 | 0 | |a Chapter 1: Introduction to Machine Learning and R -- Chapter 2: Data Exploration and Preparation -- Chapter 3: Sampling and Resampling Techniques -- Chapter 4: Data Visualization in R -- Chapter 5: Feature Engineering -- Chapter 6: Machine Learning Theory and Practice -- Chapter 7: Machine Learning Model Evaluation -- Chapter 8: Model Performance Improvement -- Chapter 9: Time Series Modelling -- Chapter 10: Scalable Machine Learning and related technology -- Chapter 11: Deep Learning Models using Keras and TensorFlow. | |
| 520 | |a Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. You will: Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on June 26, 2023). | ||
| 590 | |a Knovel |b ACADEMIC - Software Engineering | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a R (Computer program language) |2 fast |0 (OCoLC)fst01086207 | |
| 700 | 1 | |a Singh, Abhishek, |d 1976- |e author. | |
| 776 | 0 | 8 | |i Print version: |a Ramasubramanian, Karthik. |t Machine Learning Using R : With Time Series and Industry-Based Use Cases in R. |d Berkeley, CA : Apress L.P., ©2018 |z 9781484242148 |
| 856 | 4 | 0 | |u https://appknovel.uam.elogim.com/kn/resources/kpMLURWTS3/toc |z Texto completo |
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| 938 | |a YBP Library Services |b YANK |n 15901244 | ||
| 994 | |a 92 |b IZTAP | ||