509 Best + Free Artificial Intelligence Courses & Certification [2020][UPDATED]

As featured on Harvard EDU, Stackify and Inc - CourseDuck identifies and rates the Best Artificial Intelligence (AI) Courses, Tutorials, Providers and Certifications, based on 12,000+ student reviews, public mentions, recommendations, ratings and polling 5,000+ highly active StackOverFlow members. Learn more

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411 Filtered Courses
Best Free Course

1 )

Intro to Artificial Intelligence (2014)

This Udacity course takes an introductory look at industry-applicable uses of artificial intelligence. It looks at machine learning, robotics, computer vision and probabilistic reasoning. The concepts can be applied to healthcare, IoT and business leadership.
    • Course is challenging, but students who finish will be versed in advanced concepts of artificial intelligence.
    • Course is great for exploring ideas and applications of AI, which is applicable to fields outside of computer science.
    • Course is taught by world leaders in the field.
    • Course is very challenging and will alienate plenty of students.
    • Course does not succeed without the additional textbook.
    • Course is old. AI and teaching have advanced since its creation.
Best Practical Course

2 )

UC Berkeley CS188 Intro to AI (2012)

This UC Berkeley class is a semester look at the introductory concepts of artificial intelligence. It covers search, minimax, Markov decision processes, Bayes Nets and advanced applications. It will prepare students for deeper dives into programming AI through various techniques.
    • One of the best-established introductory courses in AI.
    • Course is challenging, but the trial by fire creates a strong foundation for AI.
    • Course teaches students how to train Pacman AI to show visible results for their improvements as they go.
    • Despite being an intro to AI, this course is not an intro to computer science. A coding background is pretty much mandatory.
    • Course pace is relentless.
    • Course relies heavily on students seeking their own supplemental resources.
Best Crash Course

3 )

Artificial Intelligence (2019)

Made by the legendary CrashCourse team, this YouTube tutorial introduces the ideas of AI to people who arent necessarily pursuing a career in computer science. It deeply covers many topics without forcing viewers to master the coding skills necessary to apply all of the concepts.
    • Course goes way beyond typical AI discussions and looks into ethics and metaphysical considerations.
    • Course does an amazing job of making such an advanced topic accessible to non-experts.
    • Course has fun with deep topics.
    • Course is fun but not very practical. You cannot apply these lessons by themselves.
    • With over three hours of content, this is a potentially steep investment of time for a cursory glance at AI.
    • Course goes too far into fictional concepts of AI and potentially misleads students.
Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners | Edureka
Best NEW Course

5 )

Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners | Edureka (2019)

This Edureka video on "Artificial Intelligence" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Elements of AI
Best Text Based Course

6 )

Elements of AI (2018)

The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can't) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.
Best Advanced Course

7 )

MIT OpenCourseWare - Artificial Intelligence (2010)

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
Artificial Intelligence: A Modern Approach

8 )

Artificial Intelligence: A Modern Approach (2009)

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.According to an article in The New York Times , the course on artificial intelligence is one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world. One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom.
Microsoft Professional Program for Artificial Intelligence

9 )

Microsoft Professional Program for Artificial Intelligence (2019)

Made up of three units and a final project, the Microsoft Professional Program Certificate in Artificial Intelligence provides a comprehensive program of study in AI. Learners can choose from different courses. For example, in Unit 3 - Applied Artificial Intelligence - you can choose between Computer Vision and Image Analysis or Speech Recognition Systems or Natural Language Processing (NLP).
Natural Language Processing in Action - Understanding, analyzing, and generating text with Python

10 )

Natural Language Processing in Action - Understanding, analyzing, and generating text with Python (2019)

Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.

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