Ben Cryer

www.simpliv.com

Introduction to Different Machine Leaning Tools

Machine Learning is one of the emerging technologies of the present IT industry. This technology has now become the talk of the town and has seen  an abnormally high growth over the few years.
Machine Learning technology, which  wasn’t even heard about just a few years back, has now become one of the mainstream technologies that  many businesses are willing to adapt  to enhance their business.
Many Machine Learning tools are now revolutionizing the various sectors of the global economy. But before learning  which  those tools are, let us first define what is Machine Learning.
What is Machine Learning?
Machine Learning is a technology that allows the systems to learn directly from examples, data, and experience. It is a subset of Artificial Intelligence (AI). It allows the machines to learn automatically, and improve from experience without being explicitly programmed.
Machine Learning is considered as a technology of the present and future. It has shown its impact on various sectors of this world and is the reason why so many professionals are willing to make a career in this field of technology. Any professional can learn in-depth of Machine Learning through these online courses.
Now let look into various Machine Learning tools:
1) TensorFlow: TensorFlow is a JavaScript library that helps in Machine Learning. It is an open-source library that helps to develop Machine Learning models. It has been developed by Google and has a flexible scheme of tools, libraries, and resources that help the researchers and developers in building and deploying Machine Learning applications.
TensorFlow tool is used by some of the famous companies such as eBay, Twitter, and Dropbox, etc. It supports various operating systems such as Linux, Mac OS, and Windows.
2) Scikit-learn: Scikit-learn is an open-source library for Machine Learning. It is used for Machine Learning development in Python. It supports various operating systems such as Linux, Mac OS, and Windows and helps in Data Mining and Data Analysis.
Scikit-learn includes several models of Machine Learning such as regression, classification, clustering, and reduction of dimensionality.
3) PyTorch: PyTorch is a Torch based, Python Machine Learning library. It is written using some of the programming languages including Python, C++, CUDA, etc.
PyTorch was developed by Facebook’s AI research group in 2016 and  offers Dynamic Computational Graph wherein you can modify the graph on the go with the help of Autograd. This tool has better performance comparatively and according to GitHub repo Benchmark on Deep Learning Frameworks and GPU’s, PyTorch is faster compared to other frameworks in terms of images processed per second.
4) KNIME: KNIME is a tool used for Data Analytics, reporting and integration platform. It combines different components for Machine Learning and Data Mining using data pipelining concept.
KNIME helps you to create entire Data Science workflows using a drag and drop interface. This tool can be used for business intelligence, financial data analysis, and also for CRM. Besides offering these applications, it is also very easy to learn and also to deploy and install.
5) Accord.Net: Accord.Net is written in C#. This framework consists of multiple libraries encompassing a wide range of scientific computing applications such as Statistical Data Processing, Machine Learning, etc.
Accord.Net provides a large number of probability distributions, hypothesis tests, kernel tests.  It also provides support for most popular
measurement techniques.   
These are some of the prominent tools that we have discussed here. But apart from these tools, there are some more Machine Learning tools available that you can check here. These also have some unique features, such as these above-mentioned tools, and can be very useful to organizations.
Conclusion: In this article, we have introduced you to some of the important Machine Learning tools. These are the tools that help the organizations to provide their customers an effective software development solutions as per their requirements.
If you think some more tools can be supplemented to the above list, then you can send your suggestions in the comment section below.

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