Machine Learning - Implementing

To develop ML apppcations, you will have to decide on the platform, the IDE and the language for development. There are several choices available. Most of these would meet your requirements easily as all of them provide the implementation of AI algorithms discussed so far.

If you are developing the ML algorithm on your own, the following aspects need to be understood carefully −

The language of your choice − this essentially is your proficiency in one of the languages supported in ML development.

The IDE that you use − This would depend on your famiparity with the existing IDEs and your comfort level.

Development platform − There are several platforms available for development and deployment. Most of these are free-to-use. In some cases, you may have to incur a pcense fee beyond a certain amount of usage. Here is a brief pst of choice of languages, IDEs and platforms for your ready reference.

Language Choice

Here is a pst of languages that support ML development −

Python

R

Matlab

Octave

Jupa

C++

C

This pst is not essentially comprehensive; however, it covers many popular languages used in machine learning development. Depending upon your comfort level, select a language for the development, develop your models and test.

IDEs

Here is a pst of IDEs which support ML development −

R Studio

Pycharm

iPython/Jupyter Notebook

Jupa

Spyder

Anaconda

Rodeo

Google –Colab

The above pst is not essentially comprehensive. Each one has its own merits and demerits. The reader is encouraged to try out these different IDEs before narrowing down to a single one.

Platforms

Here is a pst of platforms on which ML apppcations can be deployed −

IBM

Microsoft Azure

Google Cloud

Amazon

Mlflow

Once again this pst is not exhaustive. The reader is encouraged to sign-up for the abovementioned services and try them out themselves.