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.
The pursuit of educational inclusion brings us into close contact with an absolute wealth of wonderful organizations—motivated communities united around the noble goal of ensuring that any and all aspiring learners have the unfettered opportunity to pursue self-empowerment through education.
Women Who Code
Women Who Code is just such an organization, and we were recently able to work with their Silicon Valley chapter on an event that we hosted here at Udacity HQ.
People love to make pronouncements about a year. 2016 is the Year of … what? Data? That certainly doesn’t seem possible, given that we’ve been talking about data—both big and otherwise—for some time now. And yet, there was Glassdoor, rating Data Scientist as the #1 Best Job in America for 2016. They weren’t the only ones either. CareerCast.com put Data Scientist at #1 as well. These two studies have been so extensively cited that it’s essentially become a given that Data Scientist is one of THE hot jobs of today, and of the future as well. So how do you prepare for a data science career? Let’s begin by looking at the skills you’ll need.
“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?
Didi Chuxing may be one of the most electrifying companies in the world right now. The Chinese ride-sharing service just received a $1 billion dollar investment from Apple, and is poised to grow significantly in the coming year. Which is why we are thrilled to partner with Didi on the global Di-Tech Algorithm Competition, which boasts a $100K prize for the winner, and perhaps more exciting, the opportunity to join the Didi Research Lab and work on innovations that will affect hundreds of millions of riders.
Excited to talk about Machine Learning, Data Science, and Artificial Intelligence? Great! Us too. Ready to discover the key influencers you need to follow to stay current on all the latest happenings in these fields? Excellent! We’ve got the resources you need.
You see, at Udacity, we talk a great deal about Machine Learning, Data Science, and Artificial Intelligence. We talk to each other, we talk to our students, and we talk to the world at large. If you’re engaging in these same kinds of conversations, then you know how incredibly exciting these spaces are—everywhere you turn, there are more amazing voices with more amazing insights to contribute.
But honestly, it can be a little overwhelming!
One of the most fascinating things about Machine Learning—and those who work in the field—is the remarkable scope of what’s so rapidly becoming possible because of this technology. The applications are almost limitless, and we witness this every time we talk to a Machine Learning specialist.
Lauren Edelson is a perfect example, as you’ll see from her responses to our questions below. Lauren was gracious enough to share a great deal of insight and experience with us, and her answers cover a wide range of subjects, including computer science, the relationship of bioinformatics to Machine Learning, and Machine Learning impacts on fraud detection. When queried specially about all the different ways Machine Learning informs our modern lives, she mentioned everything from healthcare and Uber, to Spotify and the tracking of presidential election swing votes!
If you have any lingering doubts as to whether Machine Learning is an exciting field, banish those doubts, and read on!