Intro to Self-Driving Cars

BY
Udacity

Learn the fundamentals of programming a self-driving car and skills such as applying calculus in code among others.

Lavel

Intermediate

Mode

Online

Duration

74 Hours

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom
Mode of Delivery Video and Text Based

Course overview

The Intro to Self-Driving Cars by Udacity is the perfect introductory programme to help you get familiarised with the essentials of programming self-driving vehicles. The course curriculum has been carefully crafted by industry experts to help you learn and sharpen the essential skills to work on self-driving cars. The content consists of key concepts such as working with C++ and matrices, implementing Python, performance programming through C++, visualising calculus, computer vision, and machine learning. 

The Intro to Self-Driving Cars course also features real-world projects, crafted by industry experts to help you not only learn the necessary concepts but implement them in real-time as well. The curriculum features immersive content, built around a self-paced module that fits your schedule, allowing you to learn comfortably.  

Moreover, the Intro to Self-Driving Cars Nanodegree programme also provides career support, such as personal career coaches, interview preparations, and technical mentor support to help you clear your doubts easily. Here, you will learn about what goes into programming a car to self-drive, implement those concepts, and get valuable feedback on improvement. You even get guidance to create a strong resume. Upon satisfactory completion, you will receive a graduation certificate as well. 

The highlights

  • A four-month course
  • Seven-day free trial
  • Self-paced learning
  • Immersive content
  • Real-world projects
  • Career coaching
  • Student community
  • Industry-expert mentors
  • Expert project reviewers
  • Technical mentors
  • Portfolio services
  • Personalised feedback
  • Graduation certificate

Program offerings

  • Experienced project reviewers
  • Real-time projects
  • Student community
  • Personal career coaching
  • Resume services
  • Interview preparation
  • Linkedin profile review
  • Github review
  • Completion certificate.

Course and certificate fees

The fee for Intro to Self-Driving Cars is summarized as follows:

Fees componentsAmount
All Access monthly
Rs. 20,500 /month
All Access bundle
Rs. 17,425 /month
Introduction to Self-Driving CarsRs. 78,131 /one-time payment


certificate availability

Yes

certificate providing authority

Udacity

Who it is for

The Intro to Self-Driving Cars online course is for professionals who are interested in the Self-Driving Cars. 

Eligibility criteria

For enrolling in the Intro to Self-Driving Cars Nanodegree programme, candidates need to have some prior experience in programming, which can include short script-writing in any programming language. They should also have a  basic knowledge of algebra and familiarity with modifying and reading code and facing challenges with problem-solving in code.

Certificate Qualifying Details

To get the certificate, candidates have to submit the project for review, on basis of which they will receive the certificate.

What you will learn

Knowledge of python C++ Machine learning Programming skills

After completing the Intro to Self-Driving Cars course, you will: -

  • Have advanced Python skills
  • Gain the ability to apply matrices, calculus, and C++ in code for programming
  • Know the framework of a self-driving car
  • Develop the capability to use C++ for performance programming
  • Know how to navigate complex algorithms and data structures
  • Know and apply the fundamentals of computer vision and machine learning

The syllabus

Course 1: Orientation

Introduction
  • Welcome to the Intro to Self-Driving Cars Nanodegree program! We are excited to have you and hope you are looking forward to learning about this game-changing field!
Getting Help
  • You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

The Carla Chronicles: Back on Track
  • Work through the readiness assessment with Carla and her friends to make sure you are ready to begin your own personal adventure with self-driving cars!

Get Ready - Intro to Self-Driving Cars
  • While you wait for your classroom to open, refresh your math and programming skills with these helpful resources.

Course 2: Bayesian Thinking

Introduction
  • A brief introduction to Bayesian Thinking from Sebastian.

Probability
  • Learn the basics of probability - the language of robotics. This lesson will focus on the math. In later lessons you'll apply this math in Python code.

Conditional Probability
  • In order to infer meaning from noisy sensor measurements, a self driving car needs to use the math of Conditional Probability. Learn this math from Sebastian (and then apply it in the next lesson).

Programming Probability in Python
  • Your chance to learn basic Python syntax while applying what you learned about probability and conditional probability in the last two lessons.
Bayes' Rule
  • Learn about Bayes' Rule from Sebastian and get your first peek at how a self driving car uses Bayes' Rule to understand where in the world it is.
Programming Bayes' Rule and World Representations
  • In this lesson, you can expect a lot of hands-on practice programming Bayesian probability in Python, and representing a 2D world that you'll need to localize a car.
Probability Distributions
  • Learn how a robot represents it's belief about uncertain quantities using something known as a **probability distribution**.
Programming Probability Distributions
  • Apply what you've learned in this course by programming and visualizing probability distributions.
Gaussian Distributions
  • You will work with a specific continuous probability distribution called the Gaussian distribution. A Gaussian distribution helps describe uncertainty in sensor measurements and a vehicle's location.
Robot Localization
  • Sebastian Thrun will give you an overview of the theory behind localization!
Histogram Filter in Python
  • Write the `sense` and `move` functions for a 2 dimensional histogram filter in Python.

Course 3: Working with Matrices

Section Overview
  • An introduction to the amazing tools and algorithms you'll learn in this lesson.
Introduction to Kalman Filters
  • Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm and implement a one-dimensional tracker of your own.
State and Object-Oriented Programming
  • In this lesson, students will learn about representing the state of a car in programming as classes and objects and mathematically as vectors that can be changed with linear algebra!
Matrices and Transformation of State
  • Linear Algebra is a rich branch of math and a useful tool. In this lesson you'll learn about the matrix operations that underly multidimensional Kalman Filters.

Course 4: C++ Basics

C++ Getting Started
  • The differences between C++ and Python and how to write C++ code.
C++ Vectors
  • To program matrix algebra operations and translate your Python code, you will need to use C++ Vectors. These vectors are similar to Python lists, but the syntax can be somewhat tricky.
Practical C++
  • Learn how to write C++ code on your own computer and compile it into a executable program without running into too many compilation errors.
C++ Object Oriented Programming
  • Learn the syntax of C++ object oriented programming as well as some of the additional OOP features provided by the language.
Python and C++ Speed
  • In this lesson, we'll compare the execution times of C++ and Python programs.

Course 5: Performance Programming in C++

C++ Intro to Optimization
  • Optimizing C++ involves understanding how a computer actually runs your programs. You'll learn how C++ uses the CPU and RAM to execute your code and get a sense for what can slow things down.
C++ Optimization Practice
  • Now you understand how C++ programs execute. It's time to learn specific optimization techniques and put them into practice. This lesson will prepare you for the lesson's code optimization project.
Project: Optimize Histogram Filter
  • Get ready to optimize some C++ code. You are provided with a working 2-dimensional histogram filter; your job is to get the histogram filter code to run faster!

Course 6: Navigating Data Structures

How to Solve Problems
  • A systematic way of approaching and breaking down problems.
Data Structures
  • The list isn't the only structure for storing data! In this lesson you'll learn about sets, dictionaries and other Python data structures.
The Search Problem
  • When programming a car to drive itself you run into problems. Many of these are "search" problems. In this lesson you'll learn what search problems are and several algorithms for solving them.

Course 7: Vehicle Motion and Control

Odometers, Speedometers and Derivatives
  • Gain a conceptual understanding of the *derivative* and basic calculus by plotting points and finding slopes.
Accelerometers, Rate Gyros and Integrals
  • Learn how **integrals** can be used to calculate accumulated changes by finding the area under a curve.
Two Dimensional Robot Motion and Trigonometry
  • Learn the basics of trigonometry and how to decompose a self driving car's motion into X and Y components.
Reconstructing Trajectories from Sensor Data
  • Use raw acceleration, displacement, and angular rotation data from a vehicle's accelerometer, odometer, and rate gyros to reconstruct a vehicle's X, Y trajectory.

Course 8: Computer Vision and Machine Learning

Computer Vision and Classification
  • Students will learn how to program an image classifier using computer vision techniques. Along the way you'll learn about machine learning, color transformation, feature extraction, and more!

Course 9: Graduation! - Intro to Self-Driving Cars

Congratulations! You've finished!
  • Congratulations! You've reached the end of the Intro to Self-Driving Cars Nanodegree program!
Your next Nanodegree
  • Enroll in a Career-Ready Nanodegree program

Admission details

Join the Udacity Intro to Self-Driving Cars Course, follow these steps: -

  1. Go to Udacity’s official website. 

  1. Scroll to look for the “Intro to Self-Driving Cars” course. 

  1. You will go to the programme’s web page after clicking on it, where you will see the “Enroll Now” button.

  1. Click on the button, and you will be taken to the fee details section.

  1. Select the “Start Free Trial” option and you will get a choice between Quick Checkout or Sign in. New users, choose the Quick Checkout option.

  1. Fill in your email to checkout and select a preferred payment type.

  1. Confirm the payment and your free trial will begin. After this, you can continue learning with the chosen payment type.


Filling the form

For enrolling in the Intro to Self-Driving Cars course, you will have to use your Google/Facebook/Udacity account to sign in. Once done, you will have to choose your preferred payment method, and then you can confirm to start the seven-day free trial. 

How it helps

The Intro to Self-Driving Cars course by Udacity mainly focuses on helping you get prepared to move ahead into the self-driving cars engineers programme. It provides all the necessary lessons to help you master the basics of self-driving cars and how they work, simultaneously sharpening your C++, matrices, algebra, and data structuring skills. Industry experts in this course will guide you to learn the professional practices and master the basics quickly. 

The Intro to Self-Driving Cars programme will also provide you with personal career coaching, who will assist you in searching for jobs, build your resume, prepare for interviews, and master job negotiation skills. Expert project reviewers will review your projects to provide valuable feedback and tips on improving your skills. Moreover, the course mentors will also review your social profiles, demystify the hiring processes, and train you to ace the hiring stage. 

Instructors

Mr Sebastian Thrun
President
Udacity

Mr Andy Brown
Lead
Udacity

Other Bachelors

Ms Cezanne Camacho
Instructor
Freelancer

M.S, Other Masters

Mr Andrew Paster

Mr Andrew Paster
Instructor
Udacity

Mr Anthony Navarro
Product Lead
CU Denver

Ms Elecia White

Ms Elecia White
Software Engineer
Freelancer

Mr Tarin ziyaee
Director
Freelancer

FAQs

How will I get to know if this course is the right for me?

If you already possess a basic understanding of algebra and programming, and self-driving cars fascinate you, then the Udacity Intro to Self-Driving Cars programme course is just for you. This programme will help you build the core skills to dive into this amazing industry, where you will learn the basics to progress to the advanced curriculum. 

Is there any specific admission criteria?

There are no specific criteria to enrol in this Nanodegree programme, as it accepts all candidates, irrespective of a specific background or experience.

What is this programme’s structure?

The Intro to Self-Driving Cars programme is structured to have a curriculum which houses eight projects. You can complete the course within four months, by working ten hours each week. Udacity’s reviewer network will review each of your projects, and feedback will be given. If you don’t pass a project, you can keep resubmitting until you do.

Which programming language will be used for self-driving cars?

The main programming language required for self-driving cars is C++, which is also one of the primary focal points of the course. You will also learn the basics of C++ and implement them to get hands-on experience. 

How long will the Intro to Self-Driving Cars Nanodegree programme last?

The duration of the Intro to Self-Driving Cars course will depend on the payment type you choose. However, if you fail to graduate within the given time period, you can continue learning by making monthly payments.

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