Matlab is designed to work with matrices, and while you can get Matlab to work with tables and group by categorical variables (e.g., varfun), it's often terribly cumbersome and less intuitive. The primary reason, as I see it, is R's (and Python-Pandas) extensive use of data frames and reference-by-name ecosystem. While Matlab certainly remains a primary tool in much of academic science and engineering, I do not see it used extensively in data science. Why so, especially since numpy/pandas have a lot of matrix algaebra capabilities? Is Matlab/Octave that widely used in ML/data science industry? I know a lot of you work in the ML/data science industry, so I was wondering: However, I have also heard other arguments, that the reason why Matlab/Octave is still used in this course is because this course started in 2011, when python was not as popular or widely used in ML, as a result most of the algorithms was hard to get, or had to be handcoded in Python. I have used Matlab ~ 2 years back, and I am wondering if I should really go back to Matlab, because of his statement here. I did quite a bit of research before settling on learning Python, as it seemed to be more applicable for real-life problems. Andrew Ng from Stanford ( ) mentions that Matlab/Octave is widely used in the Machine learning industry to prototype. In the second lesson on Machine Learning ( ), Prof.
0 kommentar(er)
