How machine learning can improve software development itself


The activities of many globally active IT corporations prove that machine learning will be high on their lists. Be it Google, IBM or Microsoft – all of them have made machine learning an important component of their business strategies. In addition, the tech giants have been recruiting entire competence teams and acquiring machine learning and AI startups. While IT, automotive, telecommunications and media are among the pioneers of this development, more traditional industries such as the chemicals sector, logistics/transportation and pharmaceuticals are already awaiting their turn. This makes me wonder whether machine learning can offer genuine value to the field of software development itself.

Machine Learning: An Introduction to Supervised and Unsupervised Learning Algorithms


The phrase "Machine Learning" refers to the automatic detection of meaningful data by computing systems. In the last few decades, it has become a common tool in almost any task that needs to understand data from large data sets. One of the biggest application of machine learning technology is the search engine. Search engines learn how to provide the best results based on historic, trending, and relative data sets. When you look at anti-spam software, it learns how to filter email messages.

How Machine Learning Is Changing The Software Development Paradigm


Can machine learning be used to accelerate the development of traditional software development lifecycle? As artificial intelligence and other techniques get increasingly deployed as key components of modern software systems, the hybridisation of AI and ML and the resultant software is inevitable. According to a research paper from the University of Gothenburg, AI and ML technologies are increasingly being componentised and can be more easily used and reused, even by non-experts. Recent breakthroughs in software engineering have helped AI capabilities to be effectively reused via RESTful APIs as automated cloud solutions.

5 predictions for 2016 on data, analytics and machine learning


We are able to confidently predict that in 2016 more and more applications for analysing data will require less technical expertise. It is an easy prediction but, more and more data sets will be blended from different sources allowing more insights, this will be a noticeable trend that will emerge during 2016. We predict that in 2016 a new data centric semiotic, a visual language for communicating data derived information, will become stronger, grow in importance and be the engine of informatics .

5 Myths Of Machine Learning


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