Machine Learning, Data Science, and Artificial Intelligence: Influencers to Follow

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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!

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Women In Machine Learning: Lauren Edelson

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.

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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!

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How To Build A Data Analysis Portfolio That Will Get You Hired

Getting your data projects online to get hired

At Udacity, we strive to be as responsive as possible to student queries of all kinds, and virtually every member of every team gets the opportunity to speak directly with students at one time or another. This is in fact one of the most gratifying things about working at Udacity, this direct connection to our students.

When certain subjects and topics start to come up with more frequency, we often turn to a particular Udacian for insight. One subject that has definitely come up a great deal lately is the question of how to get data projects online. To speak to this matter, our own Mat Leonard—a Udacity course developer—is here to offer some thoughts and experience!

First, a bit of “official” background on Mat:

Mat Leonard earned a PhD in Physics from UC Berkeley, where he wrote his dissertation on neural activity related to short term memory. When it came time to make sense of his data, he turned to Python and the science stack including Numpy, Scikit-learn, and Pandas. He created his personal blog, Matatat.org, to publish small data projects online. For example, he explored linear regression models for predicting body fat percentage and a Bayesian approach to A/B testing.

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Hottest Jobs in 2016 #3 Data Scientist

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Few jobs have been surrounded by as much hyperbole as has Data Scientist. Most famously, the Harvard Business Review referred to is as “The Sexiest Job of the 21st Century.” With hype like that, a backlash is inevitable, and there certainly was one, with some of the more apocalyptic voices even stating that the role would be replaced completely by automation within a decade.

That’s not going to happen.

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Putting Deep Learning To Work

This is a guest post from Vincent Vanhoucke, a Principal Scientist at Google. He is a technical lead and manager in Google’s deep learning infrastructure team.

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Deep learning is a modern take on the old idea of teaching computers, instead of programming them. It has taken the world of machine learning by storm in recent years, and for good reason! Deep learning provides state-of-the-art results in many of the thorniest problems in computing, from machine perception and forecasting, to analytics and natural language processing. Our brand new Deep Learning Course, a collaboration between Google and Udacity, will have you learning and mastering these techniques in an interactive, hands on fashion, and give you the tools and best practices you need to apply deep learning to solve your own problems.

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Improving With Experience: Machine Learning in the Modern World

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In the elevators and the stairwells, at desks and in conference rooms, by the coffee machine and in the library, everyone at Udacity is talking about machine learning. Why? Because we’re launching a brand-new Machine Learning Engineer Nanodegree program, and everyone is very excited!

Machine learning is a truly unique field, in that it can seem both very complicated, and very simple. For example, compare the following two descriptions:

“Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.”

and

“Machine learning is the science of getting computers to act without being explicitly programmed.”

The first is from Wikipedia, the second is from a Stanford course description. Somewhat different flavor, no? So how can Machine Learning be both so complicated and so simple? The answer lies in its omnipresence. Machine Learning is literally everywhere.

But what IS Machine Learning?

Where did it come from, what does it mean, and why is it important?

At its core, machine learning is about making sense of large quantities of data. And note: by ‘large’, we mean LARGE—literally millions of just about everything you can count, quantify, and analyze: millions of patients, millions of students, millions of trades, millions of tweets. The sheer volume of data the modern world now produces is what makes machine learning both necessary, and possible.

Of course fields like statistics and algorithms have long aimed to summarize data for making decisions and predictions, and many of the formulas and techniques used in machine learning were developed by mathematicians centuries ago. What is new is the quantity. Increases in computational power allow us to perform analyses in hours that would have taken centuries by hand.

The result: a billion times more data than we’ve ever had before, and a billion times more power to make sense of it. How is this all made possible? Machine learning! Literally, a machine “learning” concepts from data. It learns like we do every day: it looks at experiences and observations and discerns useful information. But while we can do that based on a couple dozen experiences, machine learning can do it based on millions of experiences, all rigorously and numerically defined.

So what do machine learning engineers actually do?

Simple! Machine learning engineers build programs that dynamically perform the analyses that data scientists used to perform manually. And why is this important? Think for a moment of all the fields where data is very important. Healthcare, education, astronomy, finance, robotics, and more. Machine learning is already impacting them all, and in fact, there is virtually no field that machine learning won’t impact!

This is one of the key reasons why machine learning is so fascinating, because it’s everywhere. Often, it’s operating when we don’t even realize it. Ever used Google Translate? How about Siri? Your Facebook News Feed? All made possible through machine learning! If you know a bit about Udacity, you’ll know that our founder and CEO Sebastian Thrun himself has a long and remarkable history in the field, from founding a Master’s program at Carnegie-Mellon that evolved into a Machine Learning PhD program, to being director of the Artificial Intelligence Laboratory at Stanford University, to leading the development of the Google driverless car.

Google Translate may in fact be one of the most famous (and most utilized!) examples of machine learning in action, and Google’s description of how it works makes for a pretty classic illustration of the concepts at play:

Machine Translation is a great example of how cutting edge research and world class infrastructure come together at Google. We focus our research efforts towards developing statistical translation techniques that improve with more data and generalize well to new languages. Our large scale computing infrastructure allows us to rapidly experiment with new models trained on web-scale data to significantly improve translation quality.

The key sentence here is “techniques that improve with more data.” This is really the essence of machine learning.

In 2006, Tom Mitchell published The Discipline of Machine Learning. In it he posed the following question:

“How can we build computer systems that automatically improve with experience?”

Machine learning is the answer to this question, and it’s why we’re launching our new Machine Learning Engineer Nanodegree Program!

 

Moneyball Your Career And Become A Data Analyst!

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There is virtually no field in the modern employment landscape that does not rely on data. The Oakland Athletics made data so famous it became a Brad Pitt movie!

But when I ask you what you want to be when you grow up, are you likely to say “Data Analyst?” Probably not. Why is that?

Could it be a holdover sentiment from another era, when data really wasn’t very exciting? Say “data” to some people and it may conjure in their minds images of anonymous automatons squinting through bifocals at reams of seemingly unintelligible numbers as they sit hunched over drab desks in drab offices producing drab reports for drab enterprises that do drab things.

Or maybe it’s the idea that data only ever sits in the backseat? Data provides the numbers, but someone else goes out and gets the glory? Data cast as the perennial Cyrano de Bergerac?

Maybe data just seems too hard?

Whatever the reasons why Data Analyst may not be tip of tongue when it comes to career choices, it may be time to revise any prevailing assumptions about the field, because data has never been hotter as a career. Why? Because EVERYONE needs to know how to collect it, analyze it, contextualize it, report on it, and act on it.

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