Introduction to different Machine Learning tools

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  • Published date: November 4, 2019
    • Albama, Albama, United States

It is now common knowledge in the world of technology that Machine Learning, an offshoot or subset of Artificial Intelligence, is a game changer. We have seen many such gamechangers in the past; so, why should this one make us sit up and take notice? Simple: it is a game changer for all the industries in which it makes an impact. Machine Learning is making an impact of unseen magnitude in a swathe of activities and industries by facilitating work to an extent that was not imagined earlier.

So, which are the tools in Machine Learning? Before getting down to getting an understanding of these tools, let us first try to figure out what tools mean in this context. As with all other technologies, tools in Machine Learning too mean the same: something that aids or smooths the functioning of this technology. Tools are needed to help the programming language, or for that matter, any aspect of a technology, to enable its functions. We can think of tools as being similar to the clutch, gear and steering wheels in an automobile. In the context of Machine Learning, tools are what move the program and help them carry out its roles and objectives.
One standout feature of Machine Learning tools is that most of them are Open Source, meaning that anyone can contribute to them and enrich them. The Open Source method is considered a milestone in democratizing technologies, and it must be said, it has largely succeeded at this.
Let us look at some of the popular Open Source tools available for Machine Learning today:
TensorFlow: Any mention of Machine Learning tools is sure to draw attention for Google’s TensorFlow, one of the most popular Machine Learning tools on our planet today. The feature for which it stands out is that it allows the user to use flowgraphs to develop neural networks.
Advantage: The primary reason for TensorFlow’s popularity is that it is not only easy to use and deploy across most platforms, but is also available in many programming languages.
KNIME: Known for its drag and drop feature by which entire workflows can be created for Data Science, KNIME can accommodate a hell of a lot of features into its workflow.
Advantage: The major KNIME brings is that it makes the entire Machine Learning workflow ridiculously easy and intuitive. It simplifies even the most complex problem statements.
Keras: Keras is a Machine Learning tool that is primarily suited for creating deep learning models. Its use is best felt when the Machine Learning library needs quick and simple prototyping.
Advantage: The main advantage that Keras brings as a Machine Learning tool is that it supports both recurrent and convolutional networks. On top of this, it is amazingly simple to use.
MLFlow: MLFlow is a tool that is designed to manage many Machine Learning models. It works with any Machine Learning algorithm or library.
Advantage: MLFlow is versatile, as it can work with any ML library or algorithm and is built to manage the entire lifecycle, ranging from experimentation to deployment of Machine Learning models.
Want to learn more of these? Simpliv is the place to visit. Its large collection of online courses in Machine Learning is very helpful in aiding your learning. Please feel free to take a look. Please also let us know your thoughts about this blog. We would love to hear from you!
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