We have heard about tools used in many professions. A carpenter, a tailor, a cobbler, an electrician, a sportsman and most others, including you and me, have used our own set of tools in our professions at some or another point of time. In the same manner, Machine Learning, too, has its set of tools. They perform much of the same functions that the tools in all these other professions serve: they make work faster, smother, and easier.
With regard to Machine Learning tools, the primary goal of a tool is to help the programmer or user deliver results from a Machine Learning project in a smooth manner. In this sense, Machine Learning tools go beyond being just algorithms, although implementing algorithms is a function of Machine Learning tools. More importantly, they can arm the user with vital capabilities that can be used at any stage of a Machine Learning project to facilitate work.
Seen in this sense, Machine Learning tools work best when they:
Are adaptive;
Have been developed by using industry best practices, and
A well-knit community contributes to their development.
Now, a look at a few popular Machine Learning tools
So, which are the Machine Learning tools that are widely used across the technological world today? This is our pick of the Machine Learning tools that are both popular and meet the criteria listed above:
Scikit-learn:
Scikit-learn originated in 2007, and is essentially developed for Machine Learning. This Open Source Machine Learning tool, written in Python, can be used for a number of Machine Learning models such as classification, clustering, regression and so on.
Google Cloud ML Engine:
Best-known for its suitability for training complex models; Google Cloud ML Engine offers all the elements of Machine Learning, such as predictive modeling, deep learning, and model building and training. It cuts down companies’ response time to customer emails.
Microsoft Cognitive Toolkit:
Microsoft claims that the Microsoft Cognitive Toolkit can train deep learning algorithms to think like humans. The Microsoft Cognitive Toolkit can handle data from BrainScript, C++, or Python. It comes with a few other features, such as integrability with Azure, interoperability with NumPy, and efficient utilization of resources.
Amazon Machine Learning:
This managed service is essentially suited for helping to make predictions out of Machine Learning models. Towards facilitating this end, it uses visualization tools and wizards. Batch predictions, Data sources, Machine Learning models, Real-time predictions and Evaluations are some of its core concepts.
PyTorch:
Torch based PyTorch uses the Autograd Module for building neural networks, to do which, it provides a variety of optimization algorithms. It can be used on the cloud, and comes with tools, libraries, and distributed learning.
Do Machine Learning tools fascinate you? Are you looking for learning that will take you headlong into this field? Try this vast spread of online courses in Machine Learning from Simpliv, the learning platform. You can enroll for any of these courses to start making an impact in the area of Machine Learning. These course are designed to help you find your feet in Machine Learning.
And, before leaving, could we understand how you felt reading this blog on Machine Learning tools? Did you find it useful? Do you think anything else needed to explained here for your benefit? We want to hear from you on these.
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