• Time
    1 Term — 6 months

    Study 10 hrs/week and complete in 6 mo.

  • Classroom Opens
    May 15, 2018

    Classroom opens in 20 days.

  • Student Rating

    View all reviews ()

  • Estimated Salary

    Based on US job data

In Collaboration With
  • Amazon Web Services - 2
  • kaggle

Why Take This Nanodegree Program?

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.

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The ML/AI market will grow from $420 million in 2014 to an estimated $5.05 billion by 2020!

Features 1
Amazing content & live sessions

Effective and Engaging Content

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.

Projects with expert feedback

Beneficial and Supportive Project Review

Advance quickly and successfully through the curriculum with the support of expert reviewers whose detailed feedback will ensure you master all the right skills.

Features 2
Guaranteed Admission

AWS Credits to Deploy Your Models

Get free access to Amazon Web Services - the same platform used by Machine Learning Engineers around the globe - to build and deploy your models.

Earn a Udacity Foundation Nanodegree

Practical Career Support

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.

Learn with the Best

Arpan Chakraborty
Arpan Chakraborty


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 Leonard
Mat Leonard


Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.

Luis Serrano
Luis Serrano

Curriculum Lead

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 Cook
Alexis Cook


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 Alammar
Jay Alammar


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.

Sebastian Thrun
Sebastian Thrun


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
Ortal Arel


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.

What You Will Learn

Download Syllabus

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Machine Learning

In this program, you will master the skills necessary to become a successful Machine Learning Engineer. You will build effective machine learning models, and learn to approach and solve real-world problems across a wide array of fields.

Become a Machine Learning Engineer. Master skills by building models that solve real-world challenges.

See Details

6 months to complete

Prerequisite Knowledge

To succeed in this program, you should have experience programing in Python, and knowledge of inferential statistics, probability, linear algebra, and calculus. See detailed requirements.

  • Machine Learning Foundations

    Explore the core concepts of Machine Learning which involve understanding the nuances in your data.

    Icon project Predicting Boston Housing Prices
  • Supervised Learning

    Now that you have a background in model building, you will learn about supervised learning, a common class of methods for model construction.

    Icon project Find Donors for CharityML
  • Unsupervised Learning

    In this lesson, we will cover unsupervised learning and how it is suitable for different kinds of problem domains.

    Icon project Creating Customer Segments
  • Deep Learning

    In this lesson, we’ll cover topics in Deep Learning including Convolutional Neural Networks.

    Icon project Dog Breed Classifier
  • Reinforcement Learning

    In this lesson, we'll cover topics in Reinforcement Learning like Markov Decision Processes, Monte Carlo methods and Temporal Difference methods.

    Icon project Train a quadcopter how to fly
  • Capstone Project

    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.

    Icon project Capstone Proposal Icon project Capstone Project

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Full Program
Machine Learning Engineer


This program will teach you how to become a Machine Learning Engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more.

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FAQ – Machine Learning Engineer

  • Why should I enroll in the Machine Learning Nanodegree Program?

    Machine learning is everywhere, and is often at work even when we don't realize it. Google Translate, Siri, and Facebook News Feeds are just a few popular examples of machine learning's omnipresence. The ability to develop machines and systems that can automatically improve themselves puts machine learning at the absolute forefront of virtually any field that relies on data. If you are interested in the field of Machine Learning, and want to get hands on experience building models to topical datasets, so that you can join the pioneers who lead this field in the industry today, this program is ideal. This program is also excellent for Data Analysts who want to move into a more machine learning centric role because this program focuses specifically on building real world skills that you will be able to apply to your Machine Learning Engineer job. The goal of the Machine Learning Nanodegree program is to equip you with key skills that will prepare you to fill roles within companies seeking machine learning experts as well as those looking to introduce machine learning techniques to their organizations.

    See More Questions

Student Reviews


Become a Machine Learning Engineer

Machine Learning Engineer Nanodegree program

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