Category: Machine Learning

Test automation is an exciting field of software testing. More than 20 years of automation testing innovations have transformed how development teams produce, release, and maintain software applications. For testers, automation means no more dealing with boring and repetitive tests. For developers, it means not having to wait very long for feedback and release status. For project managers, automation means no sub-par application sees the light of day. Development teams pursue test automation in the hopes that it can make their work easier.

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A technology professional never stops learning because there’s always something new around the corner. Adding to your skills brings immense rewards, from boosting income to improving prospects and bringing satisfaction. But you need to choose the skills wisely because learning takes time and effort. While personal aptitude should matter, do not forget to pick the ones in demand. These are worth investing in because they enhance your marketability as a professional. Here are the top in-demand skills techies must consider exploring in 2021.

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The term ‘machine learning (ML) pipeline’ refers to the most efficient methodology for creating a machine learning model. It comprises multiple steps, and substantial quantities of data before deployment become a possibility – and, of course, for interventions to be made by the most central component to developing artificial intelligence: the human being. Read more below.

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All over the world in boardrooms great and small can be heard talk of artificial intelligence and machine learning. AI/ML have become the short form way of writing about it because the words are used so often, it actually reduces productivity to write it out. They are no longer just words, they are buzzwords, power words. They will be mentioned in every official communication from every company. It doesn’t matter whether the subject is manufacturing or the driest pages of a quarterly report. CEOs are extremely concerned about whether their machines are learning. That’s fine as far as it goes. Machine learning is powering a lot of what the company will be doing for the foreseeable future. However, one suspects that some executives used the terms without having a strong grasp of what they really mean. No one wants to seem like they are not keeping up with the latest trends. But not every machine needs some form of artificial intelligence to be useful. No one wants an artificially intelligent hammer that makes decisions about whether or not it will pound nails. Consider these machines that are intelligent enough just the way they are:

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Machine Learning: The use and development of computer systems that can learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Artificial Intelligence: Artificial intelligence (AI) is the computer’s ability or a robot controlled by a computer to do tasks that humans usually do because they require human intelligence and discernment. Machine Learning is a precursor which helps create use cases for Artificial Intelligence such as Intrusion Detection, Image recognition, or categorizing data. The combination of both ML and AI has a great potential to shape the future for all technological innovation.

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Would you believe it if I tell you that you are a part of one of the sources generating big data? A layperson may instantly disagree, but it is, in fact, valid for most people using smartphones. Whether you are using social media, sending money to someone online, or using a variety of mobile applications, all of your web activity leads to data generation. This data is precious to companies who want to understand their customers better and provide personalized services. For example, eCommerce giant Amazon uses your shopping history and past product search data to recommend you related products you are most likely to buy. Research shows that we generate around 2.5 quintillion bytes of data each day, which is quite huge! Well, you may wonder how can such a massive amount of data be handled. This is where Hadoop comes into the picture. Apache Hadoop is one of the earliest open-source tools to offer storage and large-scale processing of big data. As mentioned in Apache.org, Hadoop is a framework that allows for distributed processing of large data sets across clusters of computers using simple programming models. Various companies and organizations use Hadoop for research as well as production. 

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Do you have a data science interview coming up and want to know how to prepare for it? If so, you have already made some progress. Getting an interview means you have gone through the initial stages of the hiring process. This includes applying for a job and submitting your resume. You may also have taken a technical and aptitude test. Given that you have been selected for the interview, you would want to be well prepared to impress your potential employer. Given below are a few tips that will help you crush your data science interview and land the job you aspire. 

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Data science has become a part of our lives directly and indirectly. Its wide range of applications in our daily lives includes intelligent devices like Amazon’s Alexa, Siri, meteorological forecasting, customized advertisements, chatbots, recommendations on different websites, and so much more. It is really fascinating how we all have shifted to this era of data. It has revolutionized every industry and organization in some way. And, it continues to do so by adding new functionalities and providing better outputs and customer satisfaction. The multi-dimensional approach to data science has made life easier on so many levels. Nonetheless, it is not fully explored and offers a great scope of research and development. No doubt there is a high rise in job opportunities in his domain. Terms like Machine Learning, Artificial Intelligence, Neural Networks, and Deep Learning are often used interchangeably and lead to confusion among common masses. Here, we address two of these technologies, deep learning, and neural networks. Let us consider them individually.

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Almost everyone would agree that the time has come to properly differentiate between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).  This is owing to the fact that a lot of people do not know the difference between these terms. Are you also caught up in the confusion? An artificial intelligence survey conducted by a business finance and accounting company known as Sage revealed that 43% of participants in the US and 47% of participants in the UK disclosed that they did not know what Artificial Intelligence was all about. As a result, leaders in the tech industry have realized the pressing need for them to put AI and its subcategories (machine learning, deep learning) into proper business vocabulary that they would be easily understood by everyone. Fortunately, in this post, we will be looking to differentiate between AI, ML and Deep Learning. These three terms are often used interchangeably; however, they mean different things. Let’s go deeper into the nitty-gritty of these terms.

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During the past few years, the terms artificial insight and machine learning have started showing up now and again in innovation news and sites. Often the two are utilized as equivalent words, yet numerous specialists contend that they have inconspicuous yet real differences.  Furthermore, of course, the specialists in some cases differ among themselves about what those differences are.  When all is said in done, however, two things appear to be clear: first, the term artificial insight (AI) is more established than the term Machine learning with python and second, the vast majority consider machine learning to be a subset of artificial knowledge. 

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