R vs Pyhthon, in which programming language I should have expertise to handle Machine Learning Applications?
It depends on which field you want to use it. For example if you want to use it for analyzing any type of existing or collected data, such as if you want to make models then I would recommend R. As R has many ready to use packages along with ML packages such as Keras, MXNET, now it supports tensorflow too. When counting for simplicity in codes, easy syntax or systematic codes and community support, R can be a champion but Python has its own advantages such as if you mostly work on application side or computing side then it would be more efficient and optimized. As per my opinion, the problems with R in machine learning field is over as most of the ML packages that were made for python now also available for R due to its huge community support and contributions.
I have personally experienced both programming languages. Either of these languages would be good. It really depends on the need. For example irrespective of only machine learning if we need to work with other things such as plotting, then R can beat any other with ggplot2. Even in data exploration, preparation (dplyr of tidyverse package family and data.table) and in statistical analysis R is a clear winner. Even caret package's functionality is fabulous. But python's panda, scikit learn, matplotlib and tensorflow would be very useful for analysis when we need efficient and fast processing.
The paradox of choice is a common headache, I have faced it when I started ML but I would recommend everyone to choose one of the language that serve your purpose and comfortable to use.
Choice is really depends on the task we want to perform with that language. In my case I'm a big fan of R due to its vast community support and packages that makes data analysis a fun game.
Hoping these facts would help.
Both R and Python are good for Machine Learning.
- This is a list of some libraries of Python for machine learning.:
http://stackabuse.com/the-best-machine-learning-libraries-in-python/
- This is a list for R:
https://www.r-bloggers.com/what-are-the-best-machine-learning-packages-in-r/
I would recommend to go for Python because of it's scalable nature. It would be a valuable skill to have for future opportunities.
Both R and Python are equally good for Machine Learning. But see your compatibility first.
Dear Rajani,
It depends on Machine learning uses and available recipes in phyton or R.
It depends on which field you want to use it. For example if you want to use it for analyzing any type of existing or collected data, such as if you want to make models then I would recommend R. As R has many ready to use packages along with ML packages such as Keras, MXNET, now it supports tensorflow too. When counting for simplicity in codes, easy syntax or systematic codes and community support, R can be a champion but Python has its own advantages such as if you mostly work on application side or computing side then it would be more efficient and optimized. As per my opinion, the problems with R in machine learning field is over as most of the ML packages that were made for python now also available for R due to its huge community support and contributions.
I have personally experienced both programming languages. Either of these languages would be good. It really depends on the need. For example irrespective of only machine learning if we need to work with other things such as plotting, then R can beat any other with ggplot2. Even in data exploration, preparation (dplyr of tidyverse package family and data.table) and in statistical analysis R is a clear winner. Even caret package's functionality is fabulous. But python's panda, scikit learn, matplotlib and tensorflow would be very useful for analysis when we need efficient and fast processing.
The paradox of choice is a common headache, I have faced it when I started ML but I would recommend everyone to choose one of the language that serve your purpose and comfortable to use.
Choice is really depends on the task we want to perform with that language. In my case I'm a big fan of R due to its vast community support and packages that makes data analysis a fun game.
Hoping these facts would help.
both are popular programming languages and data analysis in both are becoming more similar due to various packages available online.. if you are a new data analyst looking for the right language to start with, python will be easier to understand and work with.
I would also recommend Python - it is slightly faster developed and new libraries, ready to use just out of the box appear.
If you have properly cleared and preprocessed data both are comparable good choice for ML. But if you need to collect the data and your data need some preprocessing, clearing and pre -categorisation or at least labelling - go for Python, it is fast, flexible and ... comfortable.
Both languages are worth consideration for MLP. According me, it is good to have orientation in both. But the question which will be primary for your tasks and data? I work with tables, matrixes and visualisation and R can not be overestimated. I can find there requered libraries and figure out with graphs.
As suggested by Rahul Raoniar , It totally depends on the type of application. In my opinion I highly recommend getting a bit deeper in both. R is much more simpler and easier when it comes to supervised and unsupervised learning. With the use of the Caret Package in R you can solve many complicated supervised/unsupervised learning in timely manner. You can even do deep learning. However, if you want to master image processing or deep learning using cutting edge libraries such as Keras and Inception, I think python has no competitor. Python becomes even better option, when you need to interface a a general application software related to your field (such as: ArcGIS) with your models. Most applications when allow interfacing with programming language they allow this with python and VBA.
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