PGPDM in Data Science and Machine Learning

From cell - like cubicles to now working with artificial bots, automation has made tremendous gains in the past few years and has changed the entire concept of how the modern generation works. Artificial Intelligence (AI) and Machine Learning (ML) are all set to define the workplace and work culture of tomorrow. Rapid technological advancements in recent times have led to the generation of huge amounts of data. With ever expanding digitization, the datasets keep expanding too.   The idea that fascinates all data scientists is how this data can be harvested using AI and ML. In the following paragraphs, we shall look at some of the important trends that will shape the future of data analytics in not too distant a future. Augmented Analytics This is a technology that basically makes use of machine learning to automate data preparation and presentation. Data scientists are interested in making use of augmented analytics in conjunction with human intelligence to generate pr...

Past few years have witnessed a dramatic rise in the use of big data, data analytics along with Machine Learning, deep learning and more importantly Artificial Intelligence (AI). With an increasing number of organizations realising the true potential and value of AI and implementing in their work processes, it is feared that it will cause widespread disruption in human capital and labour workforce. There are experts, however, on the other hand of the spectrum who argue that AI should not be considered a threat to human input and employment. AI is expected to bring a lot of innovation and commoditization in the modern workplace but the need for human input will always be there. The best way forward for businesses as well as IT professionals is to carefully study and understand the pros and cons, or for that matter benefits and limitations of AI to leverage its full power and potential.   The human element cannot be altogether done away with as AI can help make business manage...

A deep learning system draws textual clues from the context of images to define them without the requirement for prior human interpretations. Since its beginnings, deep learning, as both a scientific discipline and an industry, has come a long way. From cellphone assistants to pattern recognition software system, security solutions, and other applications, deep learning has become a multi-billion dollar industry poised for exponential growth over the few next years. However to attain their full potential, these softwares have to “learn” how to learn on their own. Self-Supervised Deep Learning The power and application of deep learning is all about its ability to identify different kinds of patterns like voices, faces, images, objects, and codes. AI software doesn’t understand what these things actually are, and all they perceive is digital data, and they’re quite good at that. The great computer vision competence of deep learning algorithms assists them to set these things a...