PGPDM in Data Science and Machine Learning

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Automation: The Future of Data Science and Machine Learning?

February 04, 2019

Machine learning has been one of the most significant innovations in the field of computing, and now it is considered to be capable of taking on important roles in the field of big data analytics. Big data analysis is a massive challenge from the business perspective. For instance, activities like making sense of huge volumes of diverse data formats, data preparation for analytics and filtering redundant data can consume a lot of resources. Hiring data scientists is also an expensive proposition and not within every organization’s budget. Experts believe that machine learning can automate various tasks related to analytics- both complex and routine. Automating machine learning can free up many resources that can be utilized in more complicated and innovative jobs. Automation in the Context of Information Technology In the context of Information Technology, automation is the linking of different software and systems so that they can do particular jobs without any human interf

Top 5 Deep Learning Trends That Will Dominate 2019

October 23, 2018

Leading studies predict that the Deep Learning market is likely to exceed $20 billion by 2024, growing at a compounded annual growth rate of 4 per cent. Deep learning algorithms are more likely to become more sophisticated with passing time thereby possessing the ability to take huge amount of data generated from audio, video and images and process them to make business-friendly predictions. Deep Learning will be strongly associated with modern online services where organizations will use their predictive capabilities to provide more customised offerings to their audiences around the world. In the following paragraphs we look at top 5 Deep Learning trends that are most likely to dominate the coming year. Training dataset bias and its impact on AI It is natural for human bias to creep into a majority of decision making models. The difference and variability of algorithms dealing with AI is significantly impacted by the inputs that they are fed. Data scientists are of the opin

The Future of Data Science Lays within Cloud-Based Machine Learning

September 18, 2018

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 profit

Harnessing the Power of artificial intelligence online

September 05, 2018

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 managers m

TRADITIONAL COMPUTER VISION ALONG WITH DEEP LEARNING MAKES AI BETTER

September 02, 2018

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

How to Become a Data Scientist - The Skills, Certifications and Education You Need

August 16, 2018

Data science refers to the process of extracting meaningful information and insights from huge amount of both structured and unstructured data by us ing various tools, algorithms, artificial intelligence (AI), statistics, mathematics, programming, problem-solving, and machine learning principles among others. In other words, data science is the study of where information comes from, what it represents and how this information can be efficiently processed to help businesses offer better customer experiences and thus gain a competitive advantage in the marketplace. D iscovering hidden patterns from the raw data and extracting insights and information from it can help organizations predict customer behavior in a more efficient way. This in turn can help an organization serve its customer more efficiently, rein in costs, recognize new market opportunities, offer better products and services and subsequently gain a significant advantage over its competitors. A data scientist makes

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