Comprehensive List of Top 15 Free AI Course Resources - The Complete Self-Guided Learning Journey

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Even though AI is everywhere, don’t be dismayed if you still find it confusing. It’s so new that most people are still learning about it.

We’ve poked all corners of the internet to find courses we think are smart (and often free!) These great AI classes can get anyone onto firm ground, even as this cutting-edge field gathers speed.

Many free AI courses are available online so pick one that matches your current knowledge level. You can always move up as needed.

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Introduction to Generative AI Learning Path

What We Like

• Courses are short and ideal for micro-learners.
• Learn how Google is implementing AI in its products.
• Complete the modules and earn badges.

What We Don’t Like

• As an introductory course, it doesn’t offer hands-on practice exercises.
• With it’s focus on Google, other AI developments are not covered.

A free artificial intelligence course from the search engine giant is a no-brainer. This is an introductory microlearning course that doesn’t need any technical background and is a smart one to help you learn about generative AI .

The learning path begins by introducing you to Google’s 7 AI principles and ends with the practice of responsible AI in Google Cloud’s products.

Tip

For a more technical learning path, look into Google’s free Generative AI for Developers course.

02

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Career Essentials in Generative AI

What We Like

• Introduces you to Microsoft 365 Copilot and its productivity benefits.
• Get a Professional Certificate from Microsoft and display it on your LinkedIn profile.

What We Don’t Like

• Limited interactivity with assignments or projects.

This AI course is offered on LinkedIn by Microsoft. The five-hour primer will give you a broad overview of the artificial intelligence landscape and the core concepts of generative AI.

The LinkedIn Learning path is an amalgamation of six different courses with a quiz at the end. You don’t need to log in with a LinkedIn account or a LinkedIn Learning subscription to take this course.

03

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Intro to Artificial Intelligence

What We Like

• Taught by renowned industry professionals.
• Covers Machine Learning, Probabilistic Reasoning, Robotics and Natural Language Processing.

What We Don’t Like

• The course was last updated in 2022.

The distinguished computer scientist Peter Norvig is one of the instructors of this fundamental AI course offered by Stanford University via Udacity. Though you don’t need any prior experience with AI, this is an intermediate-level course.

There are 31 lessons on topics like Game Theory, Computer Vision, and Robotics. Videos under each topic are short (a few minutes long), so you should be able to complete the AI class in one sitting.

04

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AI for Everyone: Master the Basics

What We Like

• Offers guidance on starting a career in AI on IBM tools.
• Implement your learning on a mini project at the end of the course.

What We Don’t Like

• The course is free to audit but graded assignments and a verified certificate from edX can cost $99.

This AI introductory course is offered by IBM on the edX MOOC platform. You can take this self-paced course and complete it in four weeks. It’s tailored for beginners without any background in programming or computer science.

The topics familiarize you with the technical points of neural networks and deep learning, AI ethics, bias, jobs, and societal impact.

What Is a Large Language Model?

05

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AI Chatbots without Programming

What We Like

• Learn more about IBM Watson.
• Understand how to design chatbots and monetize them.

What We Don’t Like

• Limited to the free lite version of Watson Assistant, a chatbot service available on the IBM Cloud.

IBM offers this free AI course on the edX platform. The modules are a practical introduction to chatbot creation with the free tier of Watson Assistant at the backend. The codeless approach is ideal for beginners learning AI for the first time.

Like the other free edX AI course mentioned above, you can audit the course for free, but graded assignments and a completion certificate will cost extra.

06

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AI Ready Initiative

What We Like

• The variety of free courses offered on Amazon Educate and AWS Skill Builder.
• The skill upgrades can help you tap into the AWS Educate Job Board or the AWS Emerging Talent Community.
• Courses are always updated.

What We Don’t Like

• Scope is limited to specialized Amazon cloud tools.

Amazon has designed eight free AI courses for every hue of user—from those with non-technical backgrounds to decision-makers in management. Look for courses like Introduction to Generative Artificial Intelligence on AWS Educate or Foundations of Prompt Engineering on AWS Skill Builder.

All courses are part of Amazon’s “AI Ready” initiative that seeks to offer free AI skills training to 2 million people globally by 2025. Most of these courses utilize Amazon tools like AWS Cloud, the AWS Console, and Amazon CodeWhisperer.

The 8 Best Free AI Tools

07

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AI For Everyone

What We Like

• Taught by one of the pioneers of machine learning.
• Uses case studies to explain applications of AI.
• Several topics are designed for non-technical managers.

What We Don’t Like

• For more hands-on practice, you may need supplementary resources.

Dr. Andrew Ng is one of the most well-known names in the artificial intelligence world. His engaging teaching style makes the new concepts more accessible to follow and enjoy.

The four-week course is well-structured and covers all essential topics related to AI, machine learning, and their impact on various industries. All managers could benefit from topics on building AI projects, working with the right team, and examples of roles.

What Is Deep Learning?

08

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ChatGPT Prompt Engineering for Developers

What We Like

• Covers the specific uses of prompt engineering with LLMs.
• The examples demonstrate the practical skills you need to master.

What We Don’t Like

• For a non-technical user, a basic understanding of Python is necessary.

You are assured of high-quality teaching as Isa Fulford leads this AI tutorial from OpenAI and Andrew Ng from DeepLearning.AI. They take you through the two critical principles for writing effective prompts and how to design and use solid prompts to build a custom chatbot.

This can be an essential step in your AI learning journey, as tapping into large language models (LLMs) with engineered prompts is at the core of building applications.

09

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Prompt Engineering for ChatGPT

What We Like

• The course is the first part of Vanderbilt University’s Prompt Engineering Specialization.
• Takes a deep dive into prompt engineering with techniques like Few-shot prompting, ReAct prompting etc.

What We Don’t Like

• The free tier on Coursera limits your participation in discussion forums and access to quizzes and assignments.

Use this 18-hour self-paced AI class to enhance your prompt engineering skills. The examples will guide you in working with LLMs like ChatGPT and creating effective prompts for personal and professional uses.

The six modules are comprehensive and supported with hands-on assignments. Like all Coursera courses, you can do the course for free, but other perks like the shareable completion certificate, graded assignments, and feedback are paid.

Does ChatGPT Have an App for Android and iOS Smartphones?

10

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Data Science: Machine Learning

What We Like

• Offered by Harvard University as part of its HarvardX online learning programs.
• Follows project-based learning with the design of a movie recommendation system.

What We Don’t Like

• Not for non-technical beginner as you will need some familiarity with data science concepts and Python.

To dive deeper into technical AI, you have to understand machine learning . This Harvard course on the edX platform covers popular machine-learning algorithms, teaching you how to build a movie recommendation system.

The self-paced course will take eight weeks to complete. Like most edX courses, you can audit the course for free but cannot access course materials, assignments, and the completion certificate.

11

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Introduction to Artificial Intelligence with Python

What We Like

• Gives you a solid understanding of machine learning in Python.
• Hands-on projects and clear examples show the practical side of the theory.
• Covers important concepts of classical AI along with machine learning and NLP.

What We Don’t Like

• Needs a computer science background and intermediate expertise on Python.

This Harvard course is another foundational dive into technologies like game-playing engines, handwriting recognition, and machine translation. The theoretical frameworks are taught through hands-on projects so students can ultimately design intelligent systems.

The self-paced course can take five months to complete. It’s part of Harvard’s Computer Science for Artificial Intelligence Professional Certificate Program, but you can audit the course for free like most edX courses.

12

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Transform Your Business With AI

What We Like

• Apt for companies deeply integrated with Microsoft products.

What We Don’t Like

• As an introductory lecture, it doesn’t address specific business scenarios.

Using AI as a part of a successful business strategy is the other side of today’s rapid change. These quick Microsoft lectures aim to bridge the gap between AI technology and critical decision-making.

The presentation seeks to answer the one immediate question for business leaders—what can you do to prepare your business for AI, and how can you plan and scale with it?

13

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AI For Business Specialization

What We Like

• The four distinct courses packed into one.
• Learn more about data analytics for decision making.
• Taught by experienced Wharton professors.

What We Don’t Like

• There’s no mention of course updates as new AI developments roll in.

The curriculum is business-focused, as you will learn how to deploy AI, ML, and Big Data into your organization. The one-month course can be a good fit for decision-makers who need to understand the ethical use of AI across their industry verticals.

This AI specialization track from the University of Pennsylvania consists of four courses covering strategy and governance, HR, marketing/finance, and AI fundamentals for non-data scientists. You can take the courses in any order.

14

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AI for Good Specialization

What We Like

• Gain the skills to create your own AI tools for social good.
• Intersects AI best practices with human intelligence and ethics.

What We Don’t Like

• You have to complement this introductory specialization with more niche skills elsewhere.

The AI for Good Specialization can prepare you with the knowledge and skills to make a positive difference using AI. It’s a valuable starting point if you are passionate about making a dent in air quality, wind energy, biodiversity monitoring, and disaster management.

The case studies teach through examples how you can use AI to solve some of the significant societal and environmental problems we face today.

15

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Responsible AI: Applying AI Principles with Google Cloud

What We Like

• Google Cloud partners can benefit from the actionable steps.
• Earn a badge of completion to showcase on your Google profile.

What We Don’t Like

• As a Google-focused course, it is narrow in its scope.

Though this short course is Google-centric, the principles of ethical AI remain relevant.

The course is organized into distinct modules, each taking you through the best practices followed by Google Cloud. You can then use those frameworks to mold your ethical AI strategy.

If you’re really interested in Google Cloud, be sure to check out the next Google Cloud conference .

What Is Generative AI?

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