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3 months
At 10 hrs/week
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What You Will Learn
SYLLABUS
Machine Learning Engineer
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Prerequisite Knowledge
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.
Software Engineering Fundamentals
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 PackageMachine Learning in Production
Learn how to deploy machine learning models to a production environment using Amazon SageMaker.
Deploy a Sentiment Analysis ModelMachine Learning Case Studies
Apply machine learning techniques to solve real-world tasks; explore data and deploy both built-in and custom-made Amazon SageMaker models.
Plagiarism DetectorMachine Learning Capstone
In this capstone lesson, you’ll select a machine learning challenge and propose a possible solution.
Capstone Proposal and Project
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.
Technical mentor support
Our knowledgeable mentors guide your learning and are 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. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Program OfferingsFull list of offerings included:
Enrollment Includes:
Class Content
Content co-created with Kaggle
Real-world projects
Project reviews
Project feedback from experienced reviewers
Student Services
Technical mentor support
New
Student community
Improved
Career services
Personal career coaching
New
Interview preparations
Resume services
Github review
LinkedIn profile review
Succeed with Personalized Services
We provide services customized for your needs at every step of your learning journey to ensure your success!
Experienced Project Reviewers
Project Reviewers
Technical Mentor Support
Technical Mentor Support
Personal Career Coach
Career Coaching
Get personalized feedback on your projects
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
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
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
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
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
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
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
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
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
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.
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Machine Learning Engineer
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Learn advanced machine learning techniques and algorithms, including deployment to a production environment.
Average Time
On average, successful students take null months to complete this program.
Benefits include
- Real-world projects from industry experts
- Technical mentor support
- Personal career coach & career services
STAY SHARP WHILE STAYING IN
- Special offers available worldwide to help in this challenging time
- Spend your time at home learning new, higher-paying job skills
- Commit to a brighter future by learning today
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Program Details
PROGRAM OVERVIEW - WHY SHOULD I TAKE THIS PROGRAM?
Why should I enroll?
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?
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?
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?
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?
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 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.How long is this Nanodegree program?
Access to this Nanodegree program runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our Nanodegree programs.Can I switch my start date? Can I get a refund?
Please see the Udacity Program Terms of Use and FAQs 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?
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.