At 10 hrs/week
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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.
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.
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.
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:)
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.
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)
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.
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 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.