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Intro to Self-Driving Cars

Nanodegree Program

This introductory program is the perfect way to start your journey.

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00Days13Hrs58Min08Sec

  • Estimated time
    4 Months

    At 10 hrs/week

  • Enroll by
    August 17, 2022

    Get access to classroom immediately on enrollment

  • Prerequisites
    Programming & Mathematics

What you will learn

  1. Intro to Self-Driving Cars

    4 months to complete

    In this program, you’ll sharpen your Python skills, apply C++, apply matrices and calculus in code, and touch on computer vision and machine learning. These concepts will be applied to solving self-driving car problems. At the end, you’ll be ready for our Self-Driving Car Engineer Nanodegree program!

    Prerequisite knowledge

    You should be comfortable reading and modifying code. You should also be comfortable with basic algebra.

    1. Bayesian Thinking

      Learn the framework that underlies a self-driving car’s understanding of itself and the world around it, and to see the world the way a self-driving car does.

    2. Working with Matrices

      This course will focus on two tools which are vital to self-driving car engineers: object oriented programming and linear algebra.

    3. C++ Basics

      This course is the first step in a rewarding journey towards C++ expertise. The goal is translation: get a program written in Python, and translate it into C++.

    4. Performance Programming in C++

      Explore how to write good code that runs correctly. We’ll focus primarily on low level features of C++, but we’ll discuss other best practices as well.

    5. Navigating Complex Data Structures

      Algorithmic thinking is a skill you’ll refine throughout your career. In this course you’ll focus on frequently used data structures and algorithms.

    6. Visualizing Calculus and Controls

      In this course you’ll learn basic calculus—the mathematics of continuity. You’ll also learn to use some of Python’s most popular visualization libraries.

    7. Machine Learning and Computer Vision

      In this course you’ll learn how a computer sees an image, and how we can use machine learning to teach a computer to identify images programmatically.

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.

  • 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

    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 offerings

  • Class content

    • 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.

  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve
  • 1,400+

    project reviewers

  • 2.7M

    projects reviewed

  • 88/100

    reviewer rating

  • 1.1 hours

    avg project review turnaround time

Mentors available to answer your questions.

  • Support for all your technical questions
  • Questions answered quickly by our team of technical mentors
  • 1,400+

    technical mentors

  • 0.85 hours

    median response time

Learn with the best.

Learn with the best.

  • Sebastian Thrun

    Udacity President

    Scientist, educator, inventor, and entrepreneur, Sebastian led the self-driving car project at Google X and founded Udacity, whose mission is to democratize education by providing lifelong, on-demand learning to millions of students around the world.

  • Andy Brown

    Curriculum Lead

    Andy has a bachelor's degree in physics from MIT, and taught himself to program after college (mostly with Udacity courses). He has been helping Udacity make incredible educational experiences since the early days of the company.

  • Cezanne Camacho

    Course Developer

    Cezanne is an expert in computer vision with an M.S. in Electrical Engineering from Stanford University. Inspired by anyone with the drive and imagination to learn something new, she aims to create more inclusive and effective STEM education.

  • 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.

  • Anthony Navarro

    Product Lead

    Anthony is a US Army combat veteran with an M.S. in Computer Engineering from Colorado State University. Prior to being a Product Lead at Udacity, he was a Senior Software Engineer at Lockheed Martin in their Autonomous Systems R&D division.

  • Elecia White

    Engineer, Author, Host

    Elecia is an embedded software engineer at Logical Elegance, Inc, the author of O’Reilly’s Making Embedded Systems, and host of the Embedded.fm podcast. She enjoys sharing her enthusiasm for engineering and devices.

  • Tarin Ziyaee

    Voyage, Director of AI

    As the Director of Artificial Intelligence at Voyage Auto, Tarin works to deliver low-cost, self-driving taxis. He brings a total of 14 years experience in perception and deep neural networks working with companies such as Apple.

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Intro to Self-Driving Cars Nanodegree

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    Best Value
  • Learn

    Learn the essentials of building a self-driving car, including probability, C++, machine learning, and linear algebra.
  • Average Time

    On average, successful students take 4 months to complete this program.
  • Benefits include

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

Program details

Program overview: Why should I take this program?
  • Why should I enroll?
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Enrollment and admission
  • Do I need to apply? What are the admission criteria?
  • What are the prerequisites for enrollment?
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Tuition and terms of program
  • How is this Nanodegree program structured?
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Software and hardware: What do I need for this program?
  • What software and versions will I need in this program?
  • Which version of TensorFlow, Keras, ROS, and C++ are taught in this program?

Intro to Self-Driving Cars Nanodegree

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