Machine learning is one of those technologies that seems to have a limitless capacity to affect change. It’s a sort of technological King Midas, able to turn everything it touches into algorithmic gold. Recent innovative implementations include everything from fraud prevention to agricultural systems that enable farmers to manage crops on a plant-by-plant basis.
Part of machine learning’s appeal lies in its fundamental agnosticism; it can be used in virtually any field, and towards virtually any purpose. Because of this, demand for those with machine learning skills is growing at a remarkable pace. Machine learning strategies are also emerging as an effective way for companies to gain marketplace advantage, thus rendering machine learning talent all the more sought-after. Additionally, new startups powered by machine learning and related technologies are launching with increasing frequency, further serving to widen the impact. We’ll highlight one such story below, in which a graduate of Udacity’s Machine Learning Nanodegree program used the skills he learned in the program to launch a new financial services startup.
Udacity is very excited to announce a new competition, and a new partnership!
We are proud to be the exclusive education partner—with e-commerce giant Alibaba—for a new challenge hosted by Alibaba and IJCAI2017, a top conference in AI space, and the main international gathering of researchers in the field.
Artificial intelligence. Machine learning. Self-driving cars. If you’re keeping up with the rapid changes in the technology industry, you’re seeing a bunch of terms thrown around as if they’re interchangeable—but really, there are some pretty important distinctions. In this post, we’re going to demystify the differences, and clarify the relationships, among these terms, especially artificial intelligence, machine learning, and self-driving cars. Let’s begin with a simple model for how we’ll approach this topic:
Artificial intelligence is the ‘what’.
Machine learning is the ‘how’.
Self-driving cars are the ‘why’.
The exponential rise of machine learning is as much a result of technological advancement as it is the active community growing around it. This includes researchers working on core algorithms, as well as practitioners who are pushing the boundaries of how machine learning can be applied. It also includes an increasing number of machine learning enthusiasts with atypical backgrounds who are joining the conversation, bringing in diverse experiences and points of view.
Discovering and Attracting Machine Learning Talent
The increasingly symbiotic relationship between companies that need machine learning expertise, and data science competition platforms like Kaggle, has greatly impacted how rapid advancement is being achieved. This relationship has also changed the hiring landscape. Companies today face ever-increasing pressure to innovate in order to remain competitive, and they are pursuing comparatively unconventional means for discovering and attracting new talent in order to maintain their edge. The need for machine learning talent is so great, that companies are looking far further afield than once they might have.
Projects are at the heart of our approach to learning. We believe you should learn by doing, and when you’re a Udacity student, projects are what you do. They’re how you learn, and they’re how we assess your learning. Ultimately, they’re also how you’ll demonstrate what you’ve learned. From the moment you enroll, to the moment your portfolio earns you the job offer, it’s all about projects.
Udacity projects can be hard work, and the stakes are often high. Expert project reviewers are standing by at any hour of the day, ready to deliver detailed assessments of your efforts. Between you and your Nanodegree credential, there is a path marked with projects that must be mastered before you can advance. You’ve got your work cut out for you. Sound fun?
It is! And to prove it to you, we’re going to look at five different projects from five different Nanodegree programs that are really, really fun!
“Machine Learning is everywhere.” This is a phrase we see often these days, and it’s pretty close to a genuine truism. Netflix, Amazon, Siri, Pandora, the list goes on. But it’s not just entertainment and media. It’s also everything from the post office to healthcare to traffic to security. Really close analysis suggests that, for a great many of us, virtually every moment of our lives is touched at some point by Machine Learning.
Is this a good thing?
Getting ready for a job interview has been likened to everything from preparing for battle, to gearing up to ask someone out on a date, to lining up a putt on the 18th green at The Masters. Meaning, at best, it’s nerve-racking, and at worse, it’s terrifying! Preparing for a Machine Learning interview is no different. You know you’ve got something ahead with the potential to be either really great, or really terrible. But how do you ensure your result is the great one?
It’s all about mindset, and preparation.