Machine learning (ML) is the best way to make progress towards human level artificial intelligence, which allows software applications to become more accurate in predicting results. It is the most promising technique that has profound realization in reorganizing practices pertaining to various fields viz. healthcare, education world industry, retail and manufacturing sectors, traffic and urban planning etc. The compilation and storage followed by specific training of the stored data are some of the salient features of the machine learning process that has tremendous scope in discovering novel output in various relevant fields. There are plenty of tools in ML that may help in the training of data without being explicitly programmed. Tools are categorized into- framework, platform, library, and interface. For the successful development and effective execution of ML, one can categorically manipulate various related tools. Working through such tools advances the process as applied to the various applications. In the present study, we intend to exploit recommendation engines for the development of tools that can handle the huge quantity of data. The usage of the overwhelming quantity of multimodal data and streamlining the same for its personalized usage are some of the unique features of the study. We also focus on the evaluation of a toolkit with loads of data and furthering several ML tools along with their features and use for the desired application in the relevant field.

Big data, Application Programming Interface (API), Command Line Interface (CLI), Graphic User Interface (GUI), Machine learning, Tool, Toolkits, Platform, Library, Interface

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