Become a Machine Learning Engineer
Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Deep Learning.
Enroll by July 17!
Classes start in
Study 15 hrs/week and complete in 6 mo.
In this program, you’ll master valuable machine learning skills that are in demand across countless industries. Investment levels in this space continue to rise, thousands of highly-valued startups have entered the field, and demand for machine learning talent shows no signs of leveling. Program graduates emerge uniquely prepared to excel in machine learning roles.
Learn to apply predictive models to massive data sets in fields like finance, healthcare, education, and more.
Get started learning Machine Learning through interactive content like quizzes, videos, and hands-on programs. Our learn-by-doing approach is the most effective way to learn Machine Learning skills.
Advance quickly and successfully through the curriculum with the support of expert reviewers whose detailed feedback will ensure you master all the right skills.
Draw inspiration and knowledge from your student community, and stay on track with the support of mentors directly in the classroom when you need guidance on specific challenges or projects.
Receive personalized feedback from our expert Careers Team, to help you perfect your resume, refine your LinkedIn profile, and prepare for a Machine Learning interview.
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Explore the core concepts of Machine Learning which involve understanding the nuances in your data.Predicting Boston Housing Prices
Now that you have a background in model building, you will learn about supervised learning, a common class of methods for model construction.Find Donors for CharityML
In this lesson, we will cover unsupervised learning and how it is suitable for different kinds of problem domains.Creating Customer Segments
In this lesson, we’ll cover topics in Deep Learning including Convolutional Neural Networks.Dog Breed Classifier
In this lesson, we'll cover topics in Reinforcement Learning like Markov Decision Processes, Monte Carlo methods and Temporal Difference methods.Train a quadcopter how to fly
This section has two phases. The first is the Capstone Proposal, during which you will draft a proposal outlining the domain of the problem you would like to solve, and your approach. This is followed by the Capstone Project: Here, you will leverage your newly-learned skills to solve the problem—as outlined in your proposal—by applying machine learning algorithms and techniques.CAPSTONE PROPOSALCAPSTONE PROJECT
Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.
Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.
Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.
Alexis is an applied mathematician with a Masters in computer science from Brown University and a Masters in applied mathematics from the University of Michigan. She was formerly a National Science Foundation Graduate Research Fellow.
Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at the Riyad Taqnia Fund, a $120 million venture capital fund focused on high-technology startups.
As the founder and president of Udacity, Sebastian’s mission is to democratize education. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass and more.
Ortal Arel is a former computer engineering professor. She holds a PhD in Computer Engineering from the University of Tennessee. Her doctoral research work was in the area of applied cryptography.