A few things to look at when implementing any ML related stuff:

• Availability of Basic Libraries: It's very important to have a few core libraries like scipy/numpy/scikit-learn available when starting. The vector/matrix based calculations will consume lot of time, if you start from scratch because there are hardly any libraries available in JS. But this applies only if we use ML for some application oriented use case. Ignore this if you want to just work in machine learning for research purpose.
• Peculiar features of JS: When working in JS, we can think of putting features like async etc. to work. I'm not quite sure about any specific ML algorithms that would make use of these. But you can look into those.
• Thinking in JS: I found it a little hard to think in JS when implementing a few algorithms like tf-idf. Basically the non-blocking way of coding was what I felt a bit different from what we do in Python.
• Community support: There are hardly any programmers implementing ML in JS. You might not get enough support, which is essential when you do it from scratch. But there are a bunch of small libraries, unfinished projects which basically were trials, but I see no activity there. And there are lot of assumptions those developers made when compared with the original algorithms. So, I wouldn't suggest you to start working on top of them.

Anyways, I'm not completely sure what you do use it for. These are my experiences. Hope it helps you decide!

Hi Vishnu, According to me JavaScript is not optimal choice for machine learning , I personally prefer "python" for machine learning over "JavaScript" . python,R,MAT LAB have some very good packages for ML Algorithm&Visualization . The support of such packages is quite behind in JavaScript.

JavaScript can be a good language for machine learning in the sense that it allows to concretely use machine learning everywhere and this fact will become increasingly important. It is not the language with the bigger support from the community in terms of machine learning but it is the language with the highest chance to be used in ubiquitous machine learning development as soon as AI becomes mainstream all around the world. Not because of quality considerations but just because of the fact that systems have to be distributed to customers, especially through the web, and JavaScript is a choice that makes always more sense.

If you can spend some minutes give a look to DN2A - Digital Neural Networks Architecture. You can find it here: https://github.com/dn2a/dn2a-javascript

Go python, its better for it