Brief Overview:

Yes, R can be used for machine learning and AI applications in 2018-2019. R is a powerful programming language and software environment for statistical computing and graphics, making it well-suited for data analysis and machine learning tasks.

5 Supporting Facts:

1. R has a wide range of libraries and packages specifically designed for machine learning and AI, such as caret, e1071, and randomForest.
2. R provides a flexible and interactive environment for data exploration, visualization, and modeling, making it ideal for prototyping and experimenting with machine learning algorithms.
3. R has a strong community of users and developers who contribute to the development of new tools and techniques for machine learning and AI.
4. R can easily integrate with other programming languages and tools, such as Python and SQL, allowing for seamless data processing and analysis pipelines.
5. R is widely used in academia and industry for data science and machine learning applications, making it a popular choice for professionals in the field.

Frequently Asked Questions:

1. Can R be used for deep learning?
   Yes, R can be used for deep learning tasks with the help of packages like keras and tensorflow.
2. Is R suitable for handling big data?
   R can handle big data with the help of packages like dplyr and data.table, but it may not be as efficient as other tools like Apache Spark or Hadoop.
3. Can R be used for natural language processing (NLP)?
   Yes, R has packages like tm and text2vec that can be used for NLP tasks.
4. Does R support real-time machine learning applications?
   R can be used for real-time machine learning applications, but it may not be as optimized for real-time processing as other tools like Apache Flink or Apache Storm.
5. Is R a good choice for production-level machine learning models?
   R can be used for production-level machine learning models, but it may require additional tools and frameworks for deployment and scalability.

BOTTOM LINE:

R is a versatile and powerful tool for machine learning and AI applications in 2018-2019, with a wide range of libraries and packages avAIlable for data analysis, modeling, and visualization. While it may not be the most efficient tool for handling big data or real-time processing, R is a popular choice among data scientists and analysts for its flexibility and ease of use.

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