Data is the new precious Oil in today’s business world. Data is a critical input for business decisions and it can impact the growth of organizations. As said by Charlie Berger of Oracle Corporation, “Without proper analysis, data is just text and numbers and not useful to derive actionable information. It is something that you can exploit today and something that your competitor may not have yet discovered.”
The data science community has come a long way and has matured a lot in the last 5 years. Earlier, the IT sector used to place a lot of emphasis on technologies like Java which includes Spring and Hibernate for writing and testing code. Following the advent of machine learning and data analytics, the focus shifted to technologies such as R, Python, and SAS. These technologies are being constantly deployed for algorithms in machine learning, deep learning, artificial intelligence and much more cutting-edge discoveries that have taken the world and visionaries by surprise.
Both machine learning and deep learning are forms of artificial intelligence, however, with some notable differences. While machine learning is a specific application of AI, deep learning is a distinctive form of ML.
Hearing about an interview always makes us feel jittery. But we all know quite well that the entire process is worth suffering for as you may just end up getting your dream job. A machine learning interview is no exception. It needs a whole lot of preparation and perseverance.