Which machine learning library to use

Question

I am looking for a library that, ideally, has the following features:

• implements hierarchical clustering of multidimensional data (ideally on similarity or distance matrix)

• implements support vector machines

• is in C++

• is somewhat documented (this one seems to be hardest)

I would like this to be in C++, as I am most comfortable with that language, but I will also use any other language if the library is worth it. I have googled and found some, but I do not really have the time to try them all out, so I want to hear what other people had for experiences. Please only answer if you have some experience with the library you recommend.

I could also use different libraries for the clustering and the SVM.

Answer 1

You should try Python for machine learning, rather than C++. Python is quite easy to understand, fast and easy to implement. If you are familiar with C++ then you would learn python really fast. Python has a vast number of in-built libraries, which can be used to implement probably everything, you can think of. 

You can use Python’s:

• NumPy for Numerical Computations.

• Pandas for Statistical techniques and data modeling.

• SciPy for Scientific Computation like linear algebra.

• Scikit learn for Machine learning.

• TensorFlow for Deep Learning.

• And many more

Hope this answer helps.