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Estimated Time
3 months
At 10 hrs/week
Enroll by
April 14, 2020
Get access to classroom immediately on enrollment
Prerequisites
Intermediate Python & Machine Learning Algorithms
In collaboration with
In collaboration with
What You Will Learn
Download SyllabusSYLLABUS
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
All Our 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.
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
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Interview preparations
Resume services
Github review
LinkedIn profile review
Resume shared with 95+ employers
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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
Technical Mentor Support
Personal Career Coach
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
Succeed with Personalized Services
We provide services customized for your needs at every step of your learning journey to ensure your success!
Project Reviewers
Technical Mentor Support
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.
Student Reviews
(336)
Chieko N.
I really enjoyed this program! Since I didn't have any background related to data, I first took Udacity's free courses such as Python, Statistics, SQL, Linear Algebra etc beforehand, and after I felt I got prepared I started this ND. I think it worked. Instructions were well organized, and explained with concrete and familiar examples, which helped me grab basic concepts of algorithms. At the end of each section, I had a project, where a lot of works and considerations were required. I searched and read a lot of resources that were sometimes beyond what I had learned in the lecture, which was necessary to complete the projects. I personally loved working on the projects, because reviewers always gave me precious feedback that contains a lot of suggestions and advises for further improvement as well as the words of affirmations and encouragement. It was always inspiring! Keeping up with deadlines was quite tough for me actually. I dedicated all my free time to this ND and managed to graduate on schedule. As a reward, I got skills, knowledge, and confidence that I can always learn something new! I believe this is the beginning of my new career in Data Science.
Mauricio B.
The program is an excellent refresher of ML concepts. I took a ML online class in 2014 (Andrew Ng's course) and this class was a good way to refresh the basic concepts. I am not sure how I would have performed if this was my first exposure to the themes. The exercises and projects were an excellent resource to familiarize myself with sklearn. I always wanted to become familiar with the library, but never found the focus to learn it. Now I feel very confident using it. I also appreciate the very basic refresher on numpy. Maybe a good reference could be provided to get more familiar with numpy and plotting techniques in general. I know this is outside the scope of the class, but I still feel not very knowledgeable about plots.
Manuel F.
The MLND is a great hands-on program with introductions to the key ML/AI techniques. The coding quizzes and projects showcase what's hot in tech, link out to great sources for deeper understanding, and at the same time familiarize the students with the practical and iterative type of work they can expect in ML jobs. I had started this program to enrich my ML understanding and skills, but have enjoyed working on the Udacity program so much that I decided to restart my career, moving on from consulting to the tech space. Looking forward:)
Parag A.
The lectures were engaging. The projects helped to gain a better understanding of how to apply the concepts learned. The structured and guided questionnaires help a student learn about how to approach a given problem. The Slack community was very helpful. The reviewers put real thought into providing constructive feedback and also provide additional links to improve the thought process and content of the projects even further. Hope this experience helps me get a job track change into the ML field.
pongsasit t.
This program are mainly focus on hands-on practices. So learn by doing make me really understand the way how to apply to my work. This course is really proper to data scientist who want to spread the vision and skills to be machine learning engineer. I was a data scientist working in R&D team before, but my new job will be in ML engineer role, so this course really help me understand more in engineering stuffs. (Sorry for my grammar)
Chun Yip Timothy N.
The program is a bit different than what I expect. I was expecting a deeper dive into machine learning algorithms but it is more about the machine learning development cycle. That said, the AI Programming with Python Nanodegree and Intro to ML are almost absolute essential prerequisites for this course.
Program Details
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.
PROGRAM OVERVIEW - WHY SHOULD I TAKE THIS PROGRAM?
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.
ENROLLMENT AND ADMISSION
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 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.
TUITION AND TERM OF 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.