At DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our interdisciplinary Science team applies the best of our artificial intelligence research to challenges within the natural sciences. The Science team has already made a number of high profile breakthroughs, and we have all the ingredients in place to make further significant progress over the coming years. To succeed in this role you will need to be passionate about advancing science using machine learning and other computational techniques. You'll join a spectrum of domain experts, ML research scientists, and engineers exploring a diverse set of important scientific problems in biology, physics, mathematics, and other areas.
During these last 18 months, I had many people asking me how to start in Data and AI. With more time in their hands and the opportunity to learn new skills. So I have decided to help anyone interested in learning about Artificial Intelligence, Machine Learning, and Data Science in general. These are some of the best resources I found helpful in my journey on these topics. Learning a new skill, concept, or subject is not easy and requires some discipline to make sure there is progress.
Everyone wants to excel at machine learning and data science these days -- and for good reason. Data is the new oil and everyone should be able to work with it. However, it's very difficult to become great in the field because the latest and greatest models seem too complicated. "Seem complicated" -- but they are not! If you have a thorough understanding of probability and statistics, they would be much, much easier to work with!
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.
The more I delve in data science, the more convinced I am that companies and data science practitioners must have a clear view on how to cut through the machine learning and AI hype, to implement an effective data science strategy that drives business value. This article hopes to establish a framework to conceptualize and implement effective data science projects. Showing that you as a data scientist can derive meaningful insights which improve revenue and profits will result in yourself being more valuable to the company. With highly optimized and easily implementable machine learning and deep learning libraries, any data monkey can build sophisticated AI algorithms with just a few lines of code. However, a sophisticated model does not equate to an effective model.