With the Android Operating System (OS) operating most smartphones worldwide, Android development is here to stay. In this article, we’ll go over why the Android developer ecosystem is such an exciting space, before touching on both the technical and soft skills you’ll need for an internship position.
With theaters and other public venues slowly reopening around the world, it is crucial that everyone adhere to mask-wearing to avoid further lockdowns. But how could we verify whether people really are following this rule? We could employ ushers or security personnel to act as enforcers — or we could build a computer vision application to check if everyone is wearing masks at all times, and which signals to us whenever someone isn’t.
This scenario is one of many examples of the uses of computer vision systems. In this article, we will show you how you can use APIs to embed computer vision in your own applications.
From personalized education, to cleaning the oceans, or transforming healthcare – data science is poised to continue revolutionizing how businesses operate by producing actionable insights from data. Digitalization has made it easier for companies to collect data about their internal processes, but this data can’t be used to its full potential without data scientists.
But what exactly is data science and how does one become a data scientist?
For industries where skilled professionals are in high demand, like big data, securing a role such as a data analyst internship can be an incredible opportunity. It’s an ideal way to gain additional experience that makes you more marketable to potential employers.
Let’s look at what you can expect when taking a data analyst internship.
R might not be the first language that comes to mind when thinking about NLP. Short for “natural-language processing,” NLP is the discipline of making human language processable by computers. It is a growing field with thousands of applications, some of which you probably use in your daily life. Python has become the most popular language for researching and developing NLP applications, thanks in part to its readability, its vast machine learning ecosystem, and its APIs for deep-learning frameworks. However, R can be an equally good choice if you intend to quantify your language data for NLP purposes. Below, we list some of the most useful NLP libraries in R and walk through a simple NLP programming example.