Looking to the Future

Enter heading here...The state of machine learning in 2018

Utilizing data science, Artificial Intelligence (AI), and Machine Learning (ML) as a process is increasingly popular and being embraced across a wide range of industries and applications. Educational campuses are advancing and getting inclined to use open source applications and data management software for resolving critical system neural networks.

With the onset of digital technology, data is developing aggressively. Data science is one such concept which is emerging at a rapid scale. It is an integrative blend of scientific methods, designs, and systems to grasp knowledge from data. As a result, qualitative data is overlapping quantitative data gradually. On the other hand, Machine Learning is a composition of algorithms that focus on a data set to make forecasts or implement actions in order to enhance some systems. Once these algorithms are automated with no human interruption and only mechanized control, these kinds of algorithms are more popularly known as Artificial Intelligence (AI).

In 2017, data science started making a pathway for AI and machine learning at center stage of the technology cycle. AI and ML had been trending topics for the whole year. AI applications have been increasingly used in numerous industries, including security, financial services, entertainment, automobiles, and more. There was a drastic growth in a variety of platforms like cloud machine learning and data science platforms.

In 2018, AI is gaining a momentum over various process developments. More practitioners are rising to the challenge of implementing the positive benefits of AI to the dubious lot. This is the year where we are likely to see a new focus of big data, AI, and ML on various areas like customer service, machine intelligence, process automation, workforce transformations, and more. In previous years, it was essential for data scientists and analysts to have extensive knowledge about which algorithm fits the bill. But now, machine learning and automation processes have facilitated analysts to consider different algorithms.

Educational campuses being the biggest hub of youth and hence, the top users of resources are ideal to understand such extensive platforms and explore the infinite capabilities starting from monitoring and predicting the consumption of energy, water, fuel, etc in campus.

Read more about Machine Learning