Machine Learning - Implementing

As you develop ML applications, you must choose a platform, an IDE, and a programming language. There are several options available. All provide the implementation of AI algorithms discussed before.

In order to develop your own ML algorithm, you need to be aware of the following aspects:

This is essentially your proficiency in one of the languages supported in machine learning.

Your choice of IDE will depend on your familiarity with the existing IDEs and your comfort level.

There are several development and deployment platforms available. The majority of these are free to use. However, in some cases you may be required to pay a license fee. Here is a list of languages, IDEs, and platforms you can choose from.

Language Choice

The following languages support machine learning development

  • Python

  • R

  • Matlab

  • Octave

  • Julia

  • C++

  • C

Despite not being comprehensive, this list covers many popular languages used in machine learning development. Select a language, develop your model, and test it.


Here is a list of IDEs that support machine learning

  • R Studio

  • Pycharm

  • iPython/Jupyter Notebook

  • Julia

  • Spyder

  • Anaconda

  • Rodeo

  • Google –Colab

It is recommended that the reader tries out each of these IDEs before narrowing down to a single one.


Platforms on which ML applications can be deployed

  • IBM

  • Microsoft Azure

  • Google Cloud

  • Amazon

  • Mlflow

The above list is not exhaustive. Readers are encouraged to sign up for the services mentioned above and try them out for themselves.

Frequently Asked Questions

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