Skip to content
Nanodegree Program

Intro to Machine Learning with PyTorch

Learn foundational machine learning techniques -- from data manipulation to unsupervised and supervised algorithms.
Enroll Now
  • DAYS
  • HRS
  • MIN
  • SEC
  • Estimated Time
    3 months

    At 10 hrs/week

  • Enroll by
    July 6, 2022

    Get access to the classroom immediately upon enrollment

  • Prerequisites
    Intermediate Python
In collaboration with
  • Kaggle
  • AWS

What You Will Learn


Intro to Machine Learning with PyTorch

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.

Learn foundational machine learning techniques - from data manipulation to unsupervised and supervised algorithms in PyTorch and scikit-learn.

Related Nanodegrees
Prerequisite Knowledge

To optimize your chances of success in this program, we recommend intermediate Python programming knowledge and basic knowledge of probability and statistics.

  • Supervised Learning

    In this lesson, you will learn about supervised learning, a common class of methods for model construction.

  • Deep Learning

    In this lesson, you’ll learn the foundations of neural network design and training in PyTorch.

  • Unsupervised Learning

    In this lesson, you will learn to implement unsupervised learning methods for different kinds of problem domains.

Icon - Dark upwards trend arrow
LinkedIn ranked AI Specialist as the #1 Emerging Job in 2020, with 74% annual job growth.

All Our Programs Include

Real-world projects from industry experts

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

Technical mentor support

Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track.
Career services

Career services

You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.
Flexible learning program

Flexible learning program

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.
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
Student community
Career services
Github review
Linkedin profile optimization
Succeed with personalized services.
We provide services customized for your needs at every step of your learning journey to ensure your success.
Get timely feedback on your projects.
Reviews By the numbers
1,400+ project reviewers
2.7M projects reviewed
88/100 reviewer rating
1.1 hours 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
Mentors available to answer your questions.
Mentors by the numbers
1,400+ technical mentors
0.85 hours median response time
Mentorship Services
  • Support for all your technical questions
  • Questions answered quickly by our team of technical mentors

Learn with the best

Cezanne Camacho
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
Mat Leonard


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
Luis Serrano


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
Dan Romuald Mbanga


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
Jennifer Staab


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
Sean Carrell


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
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
Jay Alammar


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
Andrew Paster


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.

Top student reviews

0.0 stars
NaN stars


NaN stars


NaN stars


NaN stars


NaN stars


NaN stars


Intro to Machine Learning with PyTorch

Get started today

  • Monthly access

    Pay as you go




    Enroll now
    • Maximum flexibility to learn at your own pace.
    • Cancel anytime.
  • - access

    Pay upfront and save an extra 0%

    for - access

    Enroll now
    • Save an extra 0% vs. pay as you go.
    • 3 months is the average time to complete this course.
    • Switch to monthly price after if more time is needed.
    • Cancel anytime.
    Best Value
  • Learn

    Build a strong foundation in Supervised, Unsupervised, and Deep Learning.
  • Average Time

    On average, successful students take 3 months to complete this program.
  • Benefits include

    • Real-world projects from industry experts
    • Technical mentor support
    • Career services

Program Details

  • Why should I enroll?
    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.
  • What jobs will this program prepare me for?
    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.
  • How do I know if this program is right for me?
    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.
  • What is the difference between Intro to Machine Learning with PyTorch, and Intro to Machine Learning with TensorFlow Nanodegree programs?
    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.
  • 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?
    It is recommended that you have the following knowledge, prior to entering the program:
    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 is a plus

    Basic knowledge of probability and statistics, including:
    • Experience calculating the probability of an event
    • Knowing how to calculate the mean and variance of a probability distribution is a plus
  • If I do not meet the requirements to enroll, what should I do?
    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:
  • 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.
  • How is this Nanodegree program structured?
    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.
  • 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 Intro to Machine Learning Nanodegree program, but I want to keep learning. Where should I go from here?
    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.
  • 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.