Expand Your Knowledge of Artificial Intelligence

Udacity is the only place to offer this kind of opportunity. We have collaborated with the best companies in the field to offer world-class curriculum with instructors. This program provides a broad introduction to the field of artificial intelligence that can help you maximize your potential as an artificial intelligence or machine learning engineer. Almost any student anywhere in the world with an internet connection can study the field of artificial intelligence through Udacity.

This program covers classical artificial intelligence techniques and algorithms including uninformed, heuristic, and adversarial search; local neighborhood & gradient free optimization; constraint satisfaction, symbolic logic, and planning; and probabilistic graphical models like Bayes nets & Hidden Markov Models.

This program is designed to build on your skills as an engineer or developer. As such, it doesn't prepare you for a specific job, but expands your skills with artificial intelligence algorithms. These skills can be applied to various applications such as video game AI, pathfinding for robots, and recognizing patterns over time like handwriting and sign language.

You will need a computer running a 64-bit operating system (most modern Windows, OS X, and Linux versions will work) with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.5 and supporting packages. Your network should allow secure connections to remote hosts (like SSH). We will provide you with instructions to install the required software packages. Udacity does not provide any hardware.

Yes! You will receive an Artificial Intelligence Nanodegree program credential after you successfully complete the program.

Many of our graduates continue on to our Robotics Engineer Nanodegree program and our Self-Driving Car Engineer Nanodegree programs. Feel free to explore other Nanodegree program options as well.

Yes! We admit students on a rolling basis, and you will automatically be added to the next available term once you've successfully enrolled.

When you enroll, you are automatically added to the next available term. Every term has a fixed start date, and content becomes available on that date.

Yes, but you won't be able to access the content, as it stays locked until your term begins. In the classroom, you'll see a countdown to your term's start date.

No, deferments are not an option. We ask that you please make sure to enroll for a term only if you are able to commit to the entire time frame.

Yes. In this way, we know exactly how many student are in a term, and can optimize our instructional and support resources accordingly. Additionally, this approach ensures a consistent and stable student body throughout the program, which fosters a deeper sense of community, and enables richer collaborations as students work together as a group.

You must have completed a course in Deep Learning equivalent to the Deep Learning Nanodegree program prior to entering the program. Additionally, you should have the following knowledge: Intermediate Python programming knowledge, including:

• Strings, numbers, and variables
• Statements, operators, and expressions
• Lists, tuples, and dictionaries
• Conditions, loops
• Generators & comprehensions
• Procedures, objects, modules, and libraries
• Troubleshooting and debugging
• Research & documentation
• Problem solving
• Algorithms and data structures

Basic shell scripting:

• Run programs from a command line
• Debug error messages and feedback
• Set environment variables
• Establish remote connections

Basic statistical knowledge, including:

• Populations, samples
• Mean, median, mode
• Standard error
• Variation, standard deviations
• Normal distribution

Intermediate differential calculus and linear algebra, including:

• Derivatives & Integrals
• Series expansions
• Matrix operations through eigenvectors and eigenvalues

Additionally, you should be able to follow and interpret pseudocode for algorithms like the example below and implement them in Python. You should also be able to informally evaluate the time or space complexity of an algorithm. For example, you should be able to explain that a for loop that does constant O(1) work on each iteration over an array of length n has a complexity of O(n).

function Hill-Climbing(problem) returns a State
    current <- Make-Node(problem.Initial-State)
    loop do
        neighbor <- a highest-valued successor of current
        if neighbor.value ≤ current.value then return current.state
        current <- neighbor

We have a number of courses and programs we can recommend that will help prepare you for the program, depending on the areas you need to address. For example:

• Intro to Machine Learning
• Intro to Data Science
• Intro to Programming Nanodegree program
• Data Analyst Nanodegree program
• Artificial Intelligence Programming with Python Nanodegree program
• Machine Learning Engineer Nanodegree program
• Deep Learning Nanodegree program

No. This is not a self-paced program. Students will need to keep pace with their peers throughout the duration of the program and complete all graduation requirements before the term end date (plus any allowed extension).

It is strongly recommended that you complete each project on time to ensure you meet graduation requirements. To graduate, you must complete, submit, and meet expectations for all required projects by the final deadline . While there is no penalty for missing a project deadline, missing one puts you at risk to be removed from the program if you do not stay on track and complete all required projects before the term ends. Finally, by keeping pace with your fellow students, you'll gain much more value from forums and Slack channels!

You will receive a free four-week extension, which is automatically applied to your account if you do not complete the program within the term. If you do not complete the program within the extension, you will be removed from the program and will no longer be able to access course content. To resume access to the course, you would need to pay the term fee again. In such case, your progress will carry over, so you will be able to continue where you left off.

No. You will retain access to the program materials for a period of time after graduation and you may download certain materials for your own records if you wish. Please note however, that students who leave the program—or who are removed from the program for failure to meet the final deadlines—prior to successfully graduating, will cease to have access.

Between instructional content, quizzes, projects, and other course-related activity, we estimate that investing 12-15 hours/week will enable you to proceed through the program at a successful pace. Students with significant prior experience may spend less time, while students with very limited prior experience may require significantly more time.

This Nanodegree program consists of one three month term. The term costs $799, paid at the beginning of the term.

No, the full tuition must be paid before the start of your term.

There is no free trial period for this program.

All current scholarship opportunities are posted on our Scholarships page.

There is a 7-day refund policy. During this time, you can visit the Settings page of your Udacity classroom where you can unenroll and request a full refund. This 7-day window begins the day the classroom opens. After the first 7 days, course fees are non-refundable.

While some of the video material is available outside of the program, most of the material will only be available to enrolled Nanodegree students. Access to project feedback, instructor support, and hiring partners are benefits exclusive to the Nanodegree programs.