Machine Learning Using R: With Time Series and Industry-Based Use Cases in R, 2/e

Karthik Ramasubramanian, Abhishek Singh

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商品描述

 

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.

What You'll Learn 

 

 

  • 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

 

Who This Book is For

Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.

商品描述(中文翻譯)

檢視使用R建立可擴展機器學習模型的最新技術進展。這本第二版書籍將向您展示如何使用機器學習算法並使用原始數據構建機器學習模型。如果您只熟悉R,您將學習如何使用R編程與TensorFlow一起使用,避免學習Python的麻煩。

與第一版一樣,作者們在這本書中保持了理論和機器學習應用的良好平衡,通過各種真實世界的應用案例,為您提供了機器學習的全面主題集合。本版新增了關於時間序列模型和深度學習的章節。

您將學到什麼

- 使用R了解機器學習算法
- 掌握構建機器學習模型的過程
- 探討機器學習算法的理論基礎
- 看到行業專注的真實世界應用案例
- 在R中處理時間序列建模
- 在R中應用Keras和TensorFlow進行深度學習

適合閱讀對象

- 想要了解使用R實踐機器學習方法/算法細微差異的數據科學家、數據科學專業人士和學術界研究人員。