At 10 hrs/week
Get access to classroom immediately on enrollment
To optimize your chances of success in this program, we recommend intermediate Python programming knowledge and intermediate knowledge of machine learning algorithms.See detailed requirements.
In this lesson, you’ll write production-level code and practice object-oriented programming, which you can integrate into machine learning projects.Build a Python Package
Learn how to deploy machine learning models to a production environment using Amazon SageMaker.Deploy a Sentiment Analysis Model
Apply machine learning techniques to solve real-world tasks; explore data and deploy both built-in and custom-made Amazon SageMaker models.Plagiarism Detector
In this capstone lesson, you’ll select a machine learning challenge and propose a possible solution.Capstone Proposal and Project
from industry experts
Personal career coach and
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 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.
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 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 is a former research mathematician specializing in Algebraic Combinatorics. He completed his PhD and Postdoctoral Fellowship at the University of Waterloo, Canada.
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 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 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.
Numbers don't lie. See what difference it makes in career searches.*
Career-seeking and job-ready graduates found a new, better job within six months of graduation.
Average salary increase for graduates who found a new, better job within six months of graduation.
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.
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.
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.
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
Intermediate Python programming knowledge, including:
Intermediate knowledge of machine learning algorithms, including:
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.
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.
The Machine Learning Engineer Nanodegree program is comprised of content and curriculum to support four (4) projects. We estimate that students can complete the program in three (3) months, working 10 hours per week.
Please see the Udacity Nanodegree program FAQs for policies on enrollment in our 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.