Did you know that machine learning powers most of the computing tools you use at home and work? Technologies such as image processing, natural language recognition, and search algorithms are all components of machine learning.
Machine learning examples include applications like social media channels that suggest new connections, streaming or search algorithms that recommend movies or products. Moreover, it also includes any software that makes suggestions based on previous decisions you have taken or actions you have performed within the software.
It seems that embedding machine learning capabilities into a system is a simple task of taking advantage of a pool of past decisions to choose from and adapt to future use case scenarios. The truth is that machine learning is not only data-consuming but also a very complex process. Most software developers need machine learning tools developed by large R&D centers if they want to provide machine learning capabilities to end-users.
Below is a list of the best machine learning tools for use by both experienced and novice users interested in machine learning, listed in alphabetical order. Also, components of machine learning include supervised, unsupervised, and reinforcement learning. In turn, these determine the applicability of a specific tool in different scenarios.
Read more here: