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.
Learn foundational machine learning techniques -- from data manipulation to unsupervised and supervised algorithms.
At 10 hrs/week
Get access to the classroom immediately upon enrollment
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.
To optimize your chances of success in this program, we recommend intermediate Python programming knowledge and basic knowledge of probability and statistics.
In this lesson, you will learn about supervised learning, a common class of methods for model construction.
In this lesson, you’ll learn the foundations of neural network design and training in PyTorch.
In this lesson, you will learn to implement unsupervised learning methods for different kinds of problem domains.
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.
You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
We provide services customized for your needs at every step of your learning journey to ensure your success.
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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.
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.
Build a strong foundation in Supervised, Unsupervised, and Deep Learning.
On average, successful students take 3 months to complete this 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.
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.
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.
Both Nanodegree programs begin with the scikit-learn machine learning library, before pivoting to either PyTorch or TensorFlow in the Deep Learning sections.
The only difference between the two programs is the deep learning framework utilized for Project 2. As such, there are accompanying lessons in each respective Nanodegree program that train you to develop machine learning models in that deep learning framework. You will complete the same project, Create an Image Classifier, in both Nanodegree programs - in PyTorch in Intro to Machine Learning with PyTorch, and in TensorFlow for Intro to Machine Learning with TensorFlow.
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
It is recommended that you have the following knowledge, prior to entering the program:
Intermediate Python programming knowledge, including:
Basic knowledge of probability and statistics, including:
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:
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 Intro to Machine Learning Nanodegree program is comprised of content and curriculum to support three (3) projects. 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.
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.
Please see the Udacity Program FAQs for policies on enrollment in our programs.
Many of our graduates continue on to our Machine Learning Engineer Nanodegree program, and after that, to the Self-Driving Car Engineer and Artificial Intelligence 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.