10 Best Free AI Courses to Take Right Now (2024)

Mukund Kapoor
By Mukund Kapoor - Author 19 Min Read
19 Min Read

AI Courses That You Can Take to Learn About Machine Learning, LLMs, Chatbots and More

Artificial intelligence (AI) is transforming industries and our daily lives. From self-driving cars to AI art generators and chatbots, AI is powering innovations that were unimaginable just a decade ago.

As AI continues its rapid development, learning about it has become crucial for people in many careers.

Fortunately, you don’t need an advanced degree or expensive courses to start with AI. There are many free resources available online. Some courses from our list even provide hands-on experience through projects and coding exercises.

So, if you’re ready to learn about AI, read on for the best free AI courses available!


Best AI Courses

This section has curated some of the top free AI courses available online.

These courses were selected based on several criteria:

  • Relevance – The course provides useful foundational knowledge about core AI concepts, tools, and applications.
  • Accessibility – No prior knowledge is required, making the course approachable for beginners.
  • Quality – The course is well-designed, engaging, and offered by a reputable provider/instructor.
  • Flexibility – Self-paced and available for free online access.
  • Hands-on – Includes interactive exercises, coding projects, or real-world examples.

Even if you’re looking to gain basic AI literacy or simply satisfy your curiosity, these courses will offer tremendous value.

By the end, you’ll have a solid baseline understanding of AI to build upon through more advanced education and hands-on experience.

So, try to take any one of these courses and comment on your learnings or your reviews to help other readers make the decision (remember the courses are not ranked you it’s recommended to check out each one of them and make your decision based on your learning requirements):

Introduction to Artificial Intelligence in Software Testing

If you’re looking for a quick and easy way to get a grip on Artificial Intelligence (AI) in software testing, you might find Introduction to Artificial Intelligence in Software Testing useful.

It’s a free tutorial created by Sujal Patel, and it’s perfect if you’re short on time – the whole thing takes less than 30 minutes to complete. We gave it a try, and here’s what we found.

The course is pretty straightforward and gets straight to the point. It starts by breaking down what AI is and how it connects with Machine Learning and Deep Learning. This is super handy, especially if you’re just dipping your toes into the world of AI in software testing.

One of the things we liked about this course is how it’s designed for a wide range of learners. Whether you’re a tester, a developer, or just someone curious about AI in software testing, there’s something here for you. The course also examines how AI can speed up and improve automated testing, which is a big deal in software development.

We think it’s great that you can finish this course quickly and then have enough knowledge to share with your colleagues or friends. You could even host a little learning session at work or with your study group.

But hey, it’s a basic course. If you’re after more detailed info, Patel has another course that goes deeper into AI in software testing. We haven’t checked that one out yet, but it might be worth a look if you find this intro course helpful.

It’s also pretty cool that so many people have already signed up for this course. And with a rating of 3.7 out of 5, it seems like a lot of them found it helpful.

Course in a Nutshell

Duration30 minutes of on-demand video
CreatorSujal Patel
LanguageEnglish (with Auto subtitles)
Course Rating3.7 out of 5
Student Enrollment29,369
Course FormatFree tutorial
Target AudienceTesters, Developers, AI Enthusiasts
Course Content5 sections, 7 lectures
Instructor Rating3.5
Instructor Experience20 years in software testing and development
Additional Instructor InfoBased in Chicago, USA; enjoys cricket and road trips
Course Ranking on PlatformNot specified
Introduction to Generative AI Learning Path

If you’re keen to dive into the world of Generative AI but not sure where to start?

Google Cloud’s Introduction to Generative AI Learning Path might be just what you’re looking for. We checked it out, and here’s our take on it.

Managed by Google Cloud, it offers a solid foundation, especially if you’re new to large language models and the principles of responsible AI.

The learning path is thoughtfully structured, starting with an introductory course that explains what Generative AI is and how it differs from traditional machine learning methods. You’ll also delve into Google’s tools for developing Gen AI, a great plus if you’re planning to apply these skills practically.

What makes this course stand out is its balance of depth and accessibility. While the initial courses are introductory, they cover significant ground in terms of content and applicability.

We think it’s pretty cool that despite being an online course, it offers a tangible reward – a skill badge if you complete the course. This could be a nice addition to your digital resume or LinkedIn profile, showing your commitment to learning and professional development.

Course in a Nutshell

DurationEach course approx. 8 hours
CreatorManaged by Google Cloud
RatingVaried (Individual course ratings not specified)
Student EnrollmentNot specified
Course FormatMicrolearning courses
Target AudienceBeginners in AI, Developers, AI Enthusiasts
Course Content5 courses including “Introduction to Generative AI”, “Introduction to Large Language Models”, etc.
Instructor RatingNot specified
Instructor ExperienceTeam of experienced professionals from Google Cloud
Additional Instructor InfoTeam focuses on practical application of Google Cloud technologies in AI
Course Ranking on PlatformNot specified
Data Science: Machine Learning by Harvard

In Data Science: Machine Learning, you will delve into the science behind some of the most successful data science techniques, like building a movie recommendation system.

The course highlights machine learning’s ability to develop predictive algorithms utilizing data, which sets it apart from previous computer-guided decision processes.

It can be used for simple tasks like text-to-speech conversion and complex ones like movie recommendations and spam identification.

Important machine learning algorithms, PCA, and regularization methods are covered in this course, which is part of the Data Science Professional Certificate Program. Constructing a movie recommendation system offers a practical example of these ideas in action.

Important takeaways include familiarity with common machine learning algorithms, knowledge of machine-learning fundamentals, and an appreciation for cross-validation as a means of avoiding overtraining.

The course instructor, Rafael Irizarry, is a renowned figure in biostatistics with an impressive background in genomics data analysis.

His involvement in the Bioconductor Project and extensive teaching experience add immense value to this course.

Course in a Nutshell

DurationOctober 18, 2023 – June 19, 2024
CreatorHarvard T.H. Chan School of Public Health
RatingNot specified (Introductory level course)
Student EnrollmentNot specified
Course FormatOnline, Self-paced
Time Commitment2 – 4 hours per week
SubjectComputer Science
Target AudienceIndividuals interested in Machine Learning, Data Science, and Algorithmic Techniques
Course ContentIncludes understanding machine learning algorithms, recommendation systems, and regularization
InstructorRafael Irizarry (Professor of Biostatistics at Harvard T.H. Chan School of Public Health)
Instructor RatingNot specified
Instructor Experience15+ years in genomics data analysis, one of the founders of the Bioconductor Project
Additional Instructor InfoSpecializes in applied statistics, with a focus on software development for genomic data analysis
Course Ranking on PlatformNot specified
IBM: AI Chatbots without Programming

IBM’s AI Chatbots without Programming course is an intriguing foray into the field, catering to those who want to learn about AI but are put off learning how to code.

The course’s high rating (4.3 out of 5) and positive reviews (all 18) attest to its usefulness and accessibility.

This self-paced course allows students to work independently over two weeks, with a time commitment of 2-4 hours each week. What is the most noticeable quality? It’s free, with a paid premium version accessible.

A record 52,125 students have already enrolled in this course, due to commence on November 16.

What sets this training apart is IBM’s offer: free access to Watson Assistant services for one year, enough to power up to ten chatbots.

This course focuses on the practical application of what is being taught. The course will teach you the fundamentals of chatbot creation, walk you through Watson Assistant’s AI features, and, most importantly, show you how to put your newfound knowledge to work for you.

The training affected its students, as seen by their testimonials.

For example, Yaron Cohen created a chatbot for his website and led a team using IBM Watson to enter second place at a company hackathon.

I’d like to thank you for the great course about chatbots. I built one and deployed it on my personal website, and two weeks ago I led a team in our company’s hackathon where we built a chatbot using IBM Watson and won the 2nd place!

Yaron Cohen

Course in a Nutshell

Duration2 weeks (2-4 hours per week, self-paced)
LanguageEnglish (with English video transcript)
Rating4.3 stars (based on 18 ratings)
Student Enrollment52,125 (as of the latest session)
Course FormatOnline, Free (Optional upgrade available)
Target AudienceFor anyone interested in AI chatbots, no programming experience is required
Course ContentModules covering chatbot fundamentals, IBM Watson Assistant, deployment, etc.
Instructor InformationCourse led by IBM professionals with expertise in AI and chatbot technology
Additional InfoSpecial offer from IBM included; skill badge upon completion
Course Ranking on PlatformNot specified
Machine Learning Specialization by Stanford

The Machine Learning Specialization by DeepLearning.AI and Stanford Online, led by the renowned Andrew Ng, is a foundational program stirring up quite the buzz in the AI community. We’re talking about a course that’s not just theoretical chit-chat; it dives deep into modern machine learning.

Now, let’s be real here. You know you’re in for something solid when you hear Andrew Ng.

This guy’s an AI visionary. He’s the big brain behind major projects at Stanford University, Google Brain, Baidu, and Landing.AI. So yeah, he knows his stuff, and he’s not teaching alone. He’s joined by Eddy Shyu, Aarti Bagul, and Geoff Ladwig – all-stars from DeepLearning.AI.

Specializations offered in this course.
Specializations are offered in this course.

Here’s the deal with the course structure: it’s split into three parts. The first course, “Supervised Machine Learning: Regression and Classification,” is like your gateway into building ML models with Python.

Next up is “Advanced Learning Algorithms.” This course is where things get a bit more intense. You’ll play around with TensorFlow, neural networks, and decision trees and even dabble with random forests and boosted trees. It’s like a playground for tech geeks but with real-world applications.

The third course, “Unsupervised Learning, Recommenders, Reinforcement Learning,” is the cherry on top. It’s all about using unsupervised learning techniques.

By the end of this specialization, you’ll not just have skimmed the surface; you’ll have dived deep into the machine-learning ocean.

Course in a Nutshell

Duration2 months (10 hours/week)
CreatorDeepLearning.AI and Stanford Online
Rating4.9 (17,355 reviews)
Student Enrollment302,836
Course FormatOnline, Self-paced Specialization (3-course series)
Target AudienceBeginners in AI and Machine Learning
Course ContentSupervised ML, Advanced Learning Algorithms, Unsupervised Learning and more
InstructorAndrew Ng (and team)
Additional Instructor InfoExpert in AI, Co-founder of Coursera, Former chief scientist at Baidu, Lead of Google Brain
Course Ranking on PlatformHighly rated (4.9 out of 5)

This course introduces modern artificial intelligence concepts and algorithms using Python. Taught by Harvard professors Gordon McKay, Brian Yu, and David Malan, it is available for free on edX but students can pay for a verified certificate.

The course covers key ideas underlying AI technologies like game engines, handwriting recognition, and machine translation.

Through hands-on projects, students learn to implement machine learning techniques like graph search algorithms, classification, optimization, and reinforcement learning in Python programs.

By the end, they gain practical experience with ML libraries and an understanding of AI principles to build their intelligent systems.

Intel’s free Introduction to AI course teaches the fundamentals of artificial intelligence and its applications over eight weeks.

The self-paced course covers machine learning, deep learning, computer vision, natural language processing, and more through video lectures and coding exercises using Python.

Designed for software developers, data scientists, and others new to AI, it allows students to earn badges for completing each module.

No prior AI experience is required, just a willingness to learn. Students can take the modules in any order and expect to spend around 90 minutes per week.

AI for Leaders from BabsonX is a self-paced course to help business leaders and executives leverage AI to advance their careers and organizations.

Through lessons, case studies, and practice sessions, leaders learn how AI can improve customer offerings, employee capabilities, operations, competitive positioning, and leadership attributes.

The course provides the PIVOT framework with 5 steps for building modern AI-powered business models. Leaders gain practical skills for competing in the age of AI and driving growth through AI platforms and networks.

It’s designed for all levels of leadership looking to implement AI in their business strategy.

Nvidia’s free course focuses specifically on computer vision, the AI capability of processing visual data. It covers the technical basics of building systems for object recognition, image classification, and more.

Additionally, it explains identifying problems suited for computer vision applications.

Since Nvidia manufactures GPUs used in AI, the course highlights their role in enabling computer vision.

The final assessment involves developing and deploying a neural network model. Expect to spend around 8 hours studying the materials to gain skills in this growing AI field.

This 14-week instructor-led Georgia Tech course provides an intermediate look at machine learning techniques.

It surveys different approaches like statistical learning, unsupervised learning, reinforcement learning, and more in-depth. Programming exercises supplement the theoretical concepts covered.

Students gain a broad understanding of machine learning algorithms and skills for developing intelligent, adaptive systems.

The course grounds techniques in real-world design and programming applications.

Upon completion, students should feel prepared to pursue more advanced machine learning education.

Ready to Learn More About AI

Well, learning about AI is not only possible through courses but with our blog, too.

If you want to stay updated with the new AI updates, ChatGPT, LLMs, and AI image generation, don’t forget to check out more resources from GreatAIPrompts.

Check out more resources:


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By Mukund Kapoor Author
Mukund Kapoor, the enthusiastic author and creator of GreatAIPrompts, is driven by his passion for all things AI. With a special knack for simplifying complex AI concepts, he's committed to helping readers of all levels - be it beginners or experts - navigate the intriguing world of artificial intelligence. Through GreatAIPrompts, Mukund ensures that readers always have access to the most recent and relevant AI news, tools, and insights. His dedication to quality, accuracy, and clarity is what sets his blog apart, making it a reliable go-to source for anyone interested in unlocking the potential of AI. For more information visit Author Bio.
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