5 Free Courses for Getting Started in Artificial Intelligence

5 Free Courses for Getting Started in Artificial Intelligence

5 Free Courses for Getting Started in Artificial Intelligence

A carefully-curated list of 5 free collections of university course material to help you better understand the various aspects of what artificial intelligence and skills necessary for moving forward in the field.

Interested in artificial intelligence? Don't know where or how to start learning?

Nothing substitutes rigorous formal education, but that's not an option for everyone for a whole host of reasons. But learning more about artificial intelligence, and the myriad overlapping and related fields and application domains does not require a PhD. Getting started can be intimidating, but don't be discouraged; check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year.

Looking for an introductory graduate school AI regimen from materials freely available online? With more and more institutes of higher learning today making the decision to allow course materials to be openly accessible to non-students via the magic of the web, all of a sudden a pseudo-university course experience can be had by almost anyone, anywhere. Have a look at the following free course materials, all of which are appropriate for an introductory level of AI understanding, some of which also cover niche application concepts and material.

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Some of these professors and their material shared below have been instrumental in shaping the minds of top AI researchers and practitioners in the world. There is no reason you cannot benefit from this same material and instruction on your own.

This could be considered the premier, pioneering, online-oriented, open-access university level AI course in existence.

Not everything is available to non UC Berkeley students (homework assignments and autograding), but the vast majority of materials are openly accessible. These materials are complete and well-organized, and include the following:

While mentioned that the homework assignments are not public, a series of progressing Pacman projects are, which cover search, reinforcement learning, classification, and beyond.

Led by professors Dan Klein and Pieter Abbeel, the lectures, videos, exams, and other materials date way back to 2014; however, it should come as no surprise that what you would learn in this course would in no way be outdated. Having consumed much of this course material several years ago myself, you can undoubtedly gain a competent overview of foundational AI topics here, including both theory and practice.

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Chief Analytics Officer Spring 2017

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May
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Chief Analytics Officer Spring 2017

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Big Data and Analytics for Healthcare Philadelphia

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May
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Big Data and Analytics for Healthcare Philadelphia

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SMX London

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May
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SMX London

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Data Science Congress 2017

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Jun
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Data Science Congress 2017

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AI Paris

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AI Paris

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