NEW!

Become a Machine Learning Engineer

We have two programs to choose from based on your experience

Start with Intro to Machine Learning if you are a beginner.
Start with Machine Learning Engineer if you already have some experience.

  • Estimated Time
    3 months for each program

    At 10 hrs/week

  • Enroll by
    May 28, 2019

    Get access to classroom immediately on enrollment

In collaboration with

Choose your Program

The Intro to Machine Learning program is for students with Python experience, and covers foundational machine learning algorithms. The Machine Learning Engineer program is for students with some ML background, and covers production and deployment.

Program One: Intro to Machine Learning

Download Syllabus
SYLLABUS

Intro to Machine Learning

Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects.

This program is intended for students with experience in Python, who have not yet studied Machine Learning topics.

Learn foundational machine learning techniques -- from data manipulation to unsupervised and supervised algorithms.

See details

3 months to complete

Program Two: Machine Learning Engineer

Download Syllabus
SYLLABUS

Machine Learning Engineer

Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in industry.

This program is intended for students who already have knowledge of machine learning algorithms.

Learn advanced machine learning deployment techniques and software engineering best practices.

See details

3 months to complete

All our Nanodegree programs include:

Real-world projects from industry experts

With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

1-on-1 technical mentor

Get a knowledgeable mentor who guides your learning and is focused on answering your questions, motivating you and keeping you on track.

Personal career coach and career services

You’ll have access to career coaching sessions, interview prep advice, and resume and online professional profile reviews to help you grow in your career.

Flexible learning program

Get a custom learning plan tailored to fit your busy life. Along with easy monthly payments you can learn at your own pace and reach your personal goals.
Succeed with Personalised Services
We provide services customised for your needs at every step of your learning journey to ensure your success!
Experienced Project Reviewers
Individual 1-on-1 Mentorship
Personal Career Coach
Experienced Project Reviewers
Reviews By the numbers
2000+ project reviewers
1.8M projects reviewed
4.85/5 reviewer ratings
3 hour avg project review turnaround time
Reviewer Services
  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve
Succeed with Personalised Services
We provide services customised for your needs at every step of your learning journey to ensure your success!
Project Reviewers
1-on-1 Mentors
Career Coaching
Experienced Project Reviewers
Reviews By the numbers
2000+ project reviewers
1.8M projects reviewed
4.85/5 reviewer ratings
3 hour avg project review turnaround time
Reviewer Services
  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve

Learn with the best

Cezanne Camacho
Cezanne Camacho

Curriculum Lead

Cezanne is a machine learning educator with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she’s applied machine learning to medical diagnostic applications.

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

Instructor

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.

Dan Romuald Mbanga
Dan Romuald Mbanga

Instructor

Dan leads Amazon AI’s Business Development efforts for Machine Learning Services. Day to day, he works with customers—from startups to enterprises—to ensure they are successful at building and deploying models on Amazon SageMaker.

Jennifer Staab
Jennifer Staab

Instructor

Jennifer has a PhD in Computer Science and a Masters in Biostatistics; she was a professor at Florida Polytechnic University. She previously worked at RTI International and United Therapeutics as a statistician and computer scientist.

Sean Carrell
Sean Carrell

Instructor

Sean Carrell is a former research mathematician specializing in Algebraic Combinatorics. He completed his PhD and Postdoctoral Fellowship at the University of Waterloo, Canada.

Josh Bernhard
Josh Bernhard

Data Scientist at Nerd Wallet

Josh has been sharing his passion for data for nearly a decade at all levels of university, and as Lead Data Science Instructor at Galvanize. He's used data science for work ranging from cancer research to process automation.

Jay Alammar
Jay Alammar

Instructor

Jay has a degree in computer science, loves visualizing machine learning concepts, and is the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.

Andrew Paster
Andrew Paster

Instructor

Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity’s Self-Driving Car Engineer Nanodegree program.

Get Started Now

Intro to Machine Learning

$399 USD

per month

Icon - Open Book - Blue
Learn
Build a solid foundation in Supervised, Unsupervised, Reinforcement, and Deep Learning.
Icon - Present - Blue
Comes with

Real world projects reviewed and graded by experienced reviewers

1-on-1 technical mentor

Personal career coach and career services

Machine Learning Engineer

$399 USD

per month

Icon - Open Book - Blue
Learn
Learn advanced machine learning techniques and algorithms, including deployment to a production environment.
Icon - Present - Blue
Comes with

Real world projects reviewed and graded by experienced reviewers

1-on-1 technical mentor

Personal career coach and career services

Program Details

    PROGRAM OVERVIEW - WHY SHOULD I TAKE THIS PROGRAM?
  • Why should I enroll?

    Intro to Machine Learning Nanodegree Program

    Machine learning is changing countless industries, from health care to finance to market predictions. Currently, the demand for machine learning engineers far exceeds the supply. In this program, you’ll apply machine learning techniques to a variety of real-world tasks, such as customer segmentation and image classification. This program is designed to teach you foundational machine learning skills that data scientists and machine learning engineers use day-to-day.

    Machine Learning Engineer Nanodegree Program

    As more and more companies are looking to build machine learning products, there is a growing demand for engineers who are able to deploy machine learning models to global audiences. In this program, you’ll learn how to create an end-to-end machine learning product. You’ll deploy machine learning models to a production environment, such as a web application, and evaluate and update that model according to performance metrics. This program is designed to give you the advanced skills you need to become a machine learning engineer.

  • What jobs will this program prepare me for?

    Intro to Machine Learning Nanodegree Program

    This program emphasizes practical coding skills that demonstrate your ability to apply machine learning techniques to a variety of business and research tasks. It is designed for people who are new to machine learning and want to build foundational skills in machine learning algorithms and techniques to either advance within their current field or position themselves to learn more advanced skills for a career transition.

    Machine Learning Engineer Nanodegree Program

    Students in the Machine Learning Engineer Nanodegree program will learn about machine learning algorithms and crucial deployment techniques, and will be equipped to fill roles at companies seeking machine learning engineers and specialists. These skills can also be applied in roles at companies that are looking for data scientists to introduce machine learning techniques into their organization.

  • How do I know if this program is right for me?

    Intro to Machine Learning Nanodegree Program

    This program assumes that you have had several hours of Python programming experience. Other than that, the only requirement is that you have a curiosity about machine learning. Do you want to learn more about recommendation systems or voice assistants and how they work? If so, then this program is right for you.

    Machine Learning Engineer Nanodegree Program

    This program assumes that you are familiar with common supervised and unsupervised machine learning techniques. As such, it is geared towards people who are interested in building and deploying a machine learning product or application. Are you interested in deploying an application that is powered by machine learning? If so, then this program is right for you.

    ENROLLMENT AND ADMISSION
  • Do I need to apply? What are the admission criteria?

    No. This Nanodegree program accepts all applicants regardless of experience and specific background.

  • What are the prerequisites for enrollment?

    Intro to Machine Learning Nanodegree Program

    It is recommended that you have the following knowledge, prior to entering the program:

    Intermediate Python programming knowledge, including:

    • At least 40hrs of programming experience
    • Familiarity with data structures like dictionaries and lists
    • Experience with libraries like NumPy and pandas is a plus

    Basic knowledge of probability and statistics, including:

    • Experience calculating the probability of an event
    • Knowing how to calculate the mean and variance of a probability distribution is a plus

    Machine Learning Engineer Nanodegree Program

    Intermediate Python programming knowledge, including:

    • At least 40hrs of programming experience
    • Familiarity with data structures like dictionaries and lists
    • Experience with libraries like NumPy and pandas

    Intermediate knowledge of machine learning algorithms, including:

    • Supervised learning models, such as linear regression
    • Unsupervised models, such as k-means clustering
    • Deep learning models, such as neural networks
  • If I do not meet the requirements to enroll, what should I do?

    Intro to Machine Learning Nanodegree Program

    You can still succeed in this program, even if you do not meet the suggested requirements. There are a few courses that can help prepare you for the program. For example:

    Machine Learning Engineer Nanodegree Program

    To succeed in this program, you are expected to know foundational machine learning algorithms. If you’d like to learn more about common unsupervised and supervised techniques, it is suggested that you take the Intro to Machine Learning Nanodegree Program.

  • Do I have to take the Intro to Machine Learning Nanodegree program before enrolling in the Machine Learning Engineer Nanodegree program?

    No. Each program is independent of the other. If you are interested in machine learning, you should look at the prerequisites for each program to help you decide where you should start your journey to becoming a machine learning engineer.

    TUITION AND TERM OF PROGRAM
  • How is this Nanodegree program structured?

    The Intro to Machine Learning Nanodegree program is comprised of content and curriculum to support three (3) projects. Once you subscribe to a Nanodegree program, you will have access to the content and services for the length of time specified by your subscription. We estimate that students can complete the program in three (3) months working 10 hours per week.

    The Machine Learning Engineer Nanodegree program is comprised of content and curriculum to support four (4) projects. Once you subscribe to a Nanodegree program, you will have access to the content and services for the length of time specified by your subscription. We estimate that students can complete the program in three (3) months working 10 hours per week.

    Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.

  • How long is this Nanodegree program?

    Access to this Nanodegree program runs for the length of time specified in your subscription plan. See the Terms of Use and FAQ for other policies around the terms of access to our Nanodegree programs.

  • Can I get a refund?

    Please see the Udacity Nanodegree program FAQs found here for policies on enrollment in our programs.

  • I have graduated from the Machine Learning Engineer 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.

    SOFTWARE AND HARDWARE - WHAT DO I NEED FOR THIS PROGRAM?
  • What software and versions will I need in this program?

    For both Nanodegree programs:

    You will need a computer running a 64-bit operating system with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.x and supporting packages.

    Most modern Windows, OS X, and Linux laptops or desktop will work well; we do not recommend a tablet since they typically have less computing power. We will provide you with instructions to install the required software packages.