Nanodegree Program

Become a Machine Learning Engineer

Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Deep Learning.

In this program you will master Supervised, Unsupervised, Reinforcement, and Deep Learning fundamentals. You will also complete a capstone project in your chosen domain.

Enroll by March 13!

  • TIME
    1 Term — 6 months

    Study 15 hrs/week and complete in 6 mo.

  • CLASSROOM OPENS
    March 13, 2018
  • Prerequisites
    Python & Mathematics

    See prerequisites in detail

  • FEE
    999€

    total

Built in partnership with
  • 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.


Why Take This Nanodegree Program?

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

Effective and Engaging Content
Effective and Engaging Content

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.

Beneficial and Supportive Project Review

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.

An Outstanding Community
An Outstanding Community

An Outstanding Community

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.

Practical Career Support

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

Instructor

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

Instructor

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

Instructor

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

Instructor

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

Instructor

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

Instructor

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
SYLLABUS

Become a Machine Learning Engineer

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 fewer details

6 months to complete

  • Machine Learning Foundations

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

    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.

    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.

    Creating Customer Segments
  • Deep Learning

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

    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.

    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.

    CAPSTONE PROPOSALCAPSTONE PROJECT
Nanodegree program
Become a Machine Learning Engineer
$999 USD

total

Learn to apply predictive models to massive data sets in fields like finance, healthcare, education, and more.

Enroll now!

Success Stories from Our Students

David

David

Madrid, Spain

Machine Learning Graduate
now at
GeeksMe

Firmware Engineer

David

David

"The confidence and skills I gained at Udacity got me hired. I am now directly applying the data workflow techniques I learned and getting great results."

Read Full Story
Daniel

Daniel

Berlin, Germany

Machine Learning Graduate
now at
Sauce Labs

iOS Reverse Engineer

Daniel

Daniel

"I really enjoyed the project-based learning, which other learning platforms don't offer. Along with the extensive feedback I received from Udacity mentors. After completing my Nanodegree and attending Intersect 2017, I decided to change careers: After 13 years at the German Armed Forces, I started working at a Testcloud Startup."

Read Full Story
Kamil

Kamil

Donauwörth, Germany

Machine Learning Graduate
now at
Airbus Helicopters

Research, Development Engineer

Kamil

Kamil

"I loved learning alongside amazing students from a diverse range of backgrounds and industries. The community empowered me to drive innovation in the aerospace industry."

Read Full Story
More Stories

FAQ

    HIGHLIGHTS
  • 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. Those skills cover Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Deep Learning.
  • Where can I find the syllabus for this course?
    Please take a look.
  • Will content from the program also be available for free outside of the Nanodegree program?
    While some of the video material is available outside of the program, most of the material will only be available to currently enrolled Nanodegree students. Access to project feedback, instructor support, and hiring partners are benefits exclusive to the Nanodegree programs.
    FEE AND PAYMENTS
  • How much does the Nanodegree program cost?
    This Nanodegree program consists of one term that is six months long. Students must complete the full six months to earn their credential and graduate. The term costs 999€, which is paid at the beginning of the term. (Fee and currency are shown based on your location.)
  • What payment methods do you accept?
    At this time we only accept credit cards in Europe. Since last year, students in Germany, Austria and Switzerland also have the option to pay via SEPA direct debit. We are working to offer you more payment options (like Paypal) in the near future.
    Please note that you can always change your payment method.
  • Is there a free trial period for this program?
    No, there is no free trial period for this program.
  • What is the refund policy?
    There is a 7-day refund policy. During this time, you can visit the Settings page of your Udacity classroom where you can unenroll and request a full refund. This 7-day window begins the day the classroom opens. After the first 7 days, course fees are non-refundable.
  • Are there any scholarships available for this program?
    All current scholarship opportunities are posted on our scholarships page.
    STRUCTURE
  • What is a Nanodegree Program?
    To read more about our Nanodegree program structure, please refer to Udacity FAQ.
  • Is this program online, in-person, or some combination of both?
    The program is online, and students interact with peers, mentors, coaches, and instructors in our virtual classrooms, in forums, and on Slack.
  • Is this program self-paced?
    It is not. This is a unique, termed program that requires students to keep pace with their peers throughout the duration of the program. If a student does not complete a term by the term deadline, they will be removed from the program and will need to re-enroll in a new term and pay the full term fee in order to continue.
  • Can I enroll in the program at any time?
    Yes! We admit students on a rolling basis, and you will automatically be added to the next available term once you've successfully enrolled. Every term has a fixed start date, and content becomes available on that date.
  • Can I enter the classroom prior to the start of my term?
    Yes, but you won't be able to access the content, as it stays locked until your term begins. In the classroom, you'll see a countdown to your term's start date.
  • What happens if I don't complete a project on time?
    It is strongly recommended that you complete each project on time to ensure you meet graduation requirements. To graduate, you must complete, submit, and meet expectations for all required projects within four months of your start date. While there is no penalty for missing a project deadline, missing one puts you at risk to be removed from the program if you do not stay on track and complete all required projects before the term ends. Finally, by keeping pace with your fellow students, you'll gain much more value from forums and Slack channels!
  • What happens if I don't complete a term by the term deadline?
    You will receive a free four-week extension, which is automatically applied to your account if you do not complete the term within the allotted six-month timeframe. If you do not complete the term within the extension, you will be removed from the program and will no longer be able to access course content. To resume access to the course, you would need to pay the term fee again. Your progress would carry over, so you would be able to continue where you left off.
  • Will I be able to pause or defer my Nanodegree program?
    No. Due to the fixed-term nature of the Machine Learning Nanodegree program, and the need for maintaining a consistent and stable student body throughout, it will not be possible to pause or defer your enrollment in this program. We ask that you please make sure to enroll for a term only if you are able to commit to the entire time frame.
  • Will I have access to the material even after the term ends?
    No. You will retain access to the program materials for a period of time after graduation and you may download certain materials for your own records if you wish. Please note however, that students who leave the program—or who are removed from the program for failure to meet deadlines—prior to successfully graduating, will cease to have access.
    PREREQUISITES
  • What are the prerequisites for enrollment?
    Prior to entering the program, you should have the following knowledge:

    Intermediate Python programming knowledge, including:
    • Strings, numbers, and variables
    • Statements, operators, and expressions
    • Lists, tuples, and dictionaries
    • Conditions, loops
    • Procedures, objects, modules, and libraries
    • Troubleshooting and debugging
    • Research & documentation
    • Problem solving
    • Algorithms and data structures


    Intermediate statistical knowledge,including:
    • Populations, samples
    • Mean, median, mode
    • Standard error
    • Variation, standard deviations
    • Normal distribution
    • Precision and accuracy
    • Hypothesis testing
    • Problem solving
    • Confidence Interval, P-values, T-test, Statistical Significance


    Intermediate calculus and linear algebra mastery, including:
    • Derivatives
    • Integrals
    • Series expansions
    • Matrix operations through eigenvectors and eigenvalues
  • What courses do you recommend if I do not meet these prerequisites?
  • What software and versions will I need in this program?
    We recommend having Anaconda installed with Python 2.7 as a minimum. Virtually any 64-bit operating with at least 8GB of RAM will be suitable.
  • Is payment due before the term begins?
    Yes. In this way, we know exactly how many student are in a term, and can optimize our instructional and support resources accordingly. Additionally, this approach ensures a consistent and stable student body throughout the program, which fosters a deeper sense of community, and enables richer collaborations as students work together as a group.
  • Can I enroll in other Nanodegree programs while I'm enrolled in the Machine Learning Nanodegree program?
    Our programs require a serious time commitment from students, so while we do not recommend doing so, we do not prohibit concurrent enrollments. This is an intensive, paced program, and students must proceed throughout the programs at the required rate of progress. To make the most of your experience, we believe you are best served by focusing on one program at a time and being fully immersed in the unique structure and pacing. You can always take one after the other!
    CAREERS
  • What jobs will this program prepare me for?
    This program can certainly be very valuable in your job and career as it's preparing you for Data Scientist and Machine Learning Engineer jobs. After successfully completing the program you'lI receive a Machine Learning Nanodegree credential and your portfolio of first-class projects will showcase your skills to potential employers.
  • Will I receive a credential when I graduate, as with other Nanodegree programs?
    Yes! You will receive a Machine Learning Nanodegree program credential after you successfully complete the program.
  • I've graduated from the Machine Learning Nanodegree program, but I want to keep learning. Where should I go from here?
    Many of our graduates continue on to our Artificial Intelligence and Self-Driving Car Engineer Nanodegree programs. Feel free to explore other Nanodegree program options as well.

Become a Machine Learning Engineer

Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Deep Learning.