Introduction to different Machine Learning tools

It is now common knowledge in the world of technology that Machine Learning, an offshootrnor subset of Artificial Intelligence, is a game changer. We have seen many suchrngamechangers in the past; so, why should this one make us sit up and take notice? Simple:rnit is a game changer for all the industries in which it makes an impact. Machine Learning isrnmaking an impact of unseen magnitude in a swathe of activities and industries byrnfacilitating work to an extent that was not imagined earlier.rnSo, which are the tools in Machine Learning? Before getting down to getting anrnunderstanding of these tools, let us first try to figure out what tools mean in this context. Asrnwith all other technologies, tools in Machine Learning too mean the same: something thatrnaids or smooths the functioning of this technology. Tools are needed to help thernprogramming language, or for that matter, any aspect of a technology, to enable itsrnfunctions. We can think of tools as being similar to the clutch, gear and steering wheels inrnan automobile. In the context of Machine Learning, tools are what move the program andrnhelp them carry out its roles and objectives.rnOne standout feature of Machine Learning tools is that most of them are Open Source,rnmeaning that anyone can contribute to them and enrich them. The Open Source method isrnconsidered a milestone in democratizing technologies, and it must be said, it has largelyrnsucceeded at this.rnLet us look at some of the popular Open Source tools available for Machine Learning today:rnTensorFlow: Any mention of Machine Learning tools is sure to draw attention for Google’srnTensorFlow, one of the most popular Machine Learning tools on our planet today. Thernfeature for which it stands out is that it allows the user to use flowgraphs to develop neuralrnnetworks.rnAdvantage: The primary reason for TensorFlow’s popularity is that it is not only easy to usernand deploy across most platforms, but is also available in many programming languages.rnKNIME: Known for its drag and drop feature by which entire workflows can be created forrnData Science, KNIME can accommodate a hell of a lot of features into its workflow.rnAdvantage: The major KNIME brings is that it makes the entire Machine Learning workflowrnridiculously easy and intuitive. It simplifies even the most complex problem statements.rnKeras: Keras is a Machine Learning tool that is primarily suited for creating deep learningrnmodels. Its use is best felt when the Machine Learning library needs quick and simplernprototyping.rnAdvantage: The main advantage that Keras brings as a Machine Learning tool is that itrnsupports both recurrent and convolutional networks. On top of this, it is amazingly simplernto use.rnMLFlow: MLFlow is a tool that is designed to manage many Machine Learning models. Itrnworks with any Machine Learning algorithm or library.rnAdvantage: MLFlow is versatile, as it can work with any ML library or algorithm and is builtrnto manage the entire lifecycle, ranging from experimentation to deployment of MachinernLearning models.rnWant to learn more of these? Simpliv is the place to visit. Its large collection of onlinerncourses in Machine Learning is very helpful in aiding your learning. Please feel free to takerna look. Please also let us know your thoughts about this blog. We would love to hear fromrnyou!rnSource link : https://www.simpliv.com/search/sub-category/machinelearning