10 Amazing Free Resources to Learn AI & Prompting
From Google courses to hands-on Kaggle projects, here’s how to build valuable AI skills without spending money.
Trying to learn about Artificial Intelligence can feel like trying to drink from a firehose. There are so many complex topics, and most university-level textbooks or courses come with a hefty price tag. If you’re a student who is curious about AI and wants to learn how to use tools like Gemini or ChatGPT effectively, it’s easy to feel stuck before you even start.
But what if you could access a world-class education in AI and prompting, taught by experts from Google, DeepLearning.AI, and top universities, all for free?
That’s the incredible reality of the modern learning landscape. This guide is built for students who are eager to learn but are on a budget. We’ve done a deep dive to find the best resources for learning AI that are genuinely free forever or offer extensive free tiers, allowing you to build valuable, career-ready skills
What Can You Actually Do With These Resources?
Before we dive into the list, it’s helpful to understand what makes these resources so game-changing for students.
Build Foundational Knowledge: Understand what AI, machine learning, and generative AI actually are, explained in simple terms.
Master the Art of Prompting: Learn how to write effective prompts to get exactly what you need from AI chatbots for your assignments and projects.
Gain Practical, Hands-On Skills: Go beyond theory by building your first AI models, analyzing data, and using real developer tools.
Earn Career-Boosting Credentials: Get free certificates and skill badges from respected organizations like Google and Kaggle to add to your resume and LinkedIn profile.
Quick Comparison Table
Tool Name
Google AI Essentials
Coursera (Audit Mode)
Elements of AI
Kaggle Learn
DeepLearning.AI
Hugging Face Courses
freeCodeCamp
fast.ai
ML Crash Course (Google)
Google AI Studio
Best For
Foundational AI Concepts
University-Level Courses
Understanding AI’s Impact
Hands-On Data Science
Expert-Led AI Courses
Natural Language Processing
In-Depth Video Courses
Practical Deep Learning
Technical Machine Learning
Experimenting with Models
Pricing Model
Completely Free
Free Course Access
Completely Free
Completely Free
Free Course Access
Completely Free
Completely Free
Completely Free
Completely Free
Free to Use
Ease of Use
Beginner
Beginner to Intermediate
Beginner
Intermediate
Beginner to Developer
Developer
Beginner to Developer
Intermediate
Intermediate
Developer
1. Google AI Essentials
Best for: Learning the absolute fundamentals of AI and Generative AI from the source.
Google offers a fantastic suite of short, high-quality introductory courses that are perfect for beginners. These courses, available on platforms like Grow with Google and Google Cloud Skills Boost, cover the core concepts of what AI is, how Large Language Models (LLMs) work, and how to use them effectively. They are designed to be accessible to a non-technical audience.
Use Case Example: A student who has heard a lot about “Generative AI” but doesn’t really understand it takes the “Introduction to Generative AI” course. In about an hour, they learn the key concepts and earn a skill badge they can add to their LinkedIn profile to show their initiative.
Pricing Details: Completely Free. All the courses in this learning path are free, and you receive a free badge of completion for each one you finish.
What You’ll Need: A web browser and a Google account.
Learning Curve: Beginner. These courses are specifically designed for people with no prior AI or coding knowledge.
What to Watch Out For: These are introductory courses. They will give you a great conceptual understanding but won’t teach you how to code complex AI models from scratch.
Beginner Tutorial: The best way to start is with the “Introduction to Generative AI” course, which you can find here on Google Cloud Skills Boost.
2. Coursera (Audit Mode)
Best for: Accessing courses from top universities and companies like Google and IBM for free.
Coursera is a massive online learning platform that hosts courses from the world’s best institutions. While you normally have to pay for a certificate, almost every course has a free “Audit” option. Auditing a course gives you access to all the video lectures, readings, and learning materials without any cost. This is an incredible way to get a world-class education on a student budget.
Use Case Example: A student wants to learn about Prompt Engineering from an expert. They enroll in the popular “Prompt Engineering for ChatGPT” course from Vanderbilt University on Coursera for free using the “Audit” option. They watch all the video lectures and learn advanced prompting techniques.
Pricing Details: Free Course Access. The “Audit” option is free for most courses. You get access to the course content but cannot submit graded assignments or receive a certificate.
What You’ll Need: A web browser.
Learning Curve: Beginner to Intermediate. It depends on the course you choose, but there are hundreds of beginner-friendly options.
What to Watch Out For: In audit mode, you won’t get a certificate to prove you completed the course, and you can’t participate in graded assignments.
Beginner Tutorial: You can find a great guide on how to enroll in courses for free using the audit method here on YouTube.
3. Elements of AI
Best for: A clear, simple, and comprehensive introduction to what AI is and its societal impact.
Created by the University of Helsinki and Reaktor, Elements of AI is a famous free online course designed to demystify artificial intelligence for everyone. It’s a non-technical course that focuses on explaining the core concepts of AI through real-world examples and simple exercises. It’s perfect for students from any field, whether you’re studying business, arts, or science.
Use Case Example: A law student wants to understand how AI might affect their future career. They take the “Introduction to AI” course and learn about the ethical implications and practical applications of AI in a way that’s relevant to their field.
Pricing Details: Completely Free. The entire “Introduction to AI” course, including all exercises and a formal certificate of completion, is 100% free.
What You’ll Need: A web browser.
Learning Curve: Beginner. No programming or complex math is required.
What to Watch Out For: The course is more theoretical and conceptual. It’s designed to make you an informed citizen, not a technical AI developer.
Beginner Tutorial: The course is self-contained, but you can see a review and overview here on YouTube.
4. Kaggle Learn
Best for: Hands-on, practical courses for learning data science and machine learning.
Kaggle is the world’s largest data science community, and its “Learn” section is a goldmine of free, interactive courses. Unlike video-based courses, Kaggle’s courses are short, hands-on tutorials where you read a concept and then immediately apply it by writing code in a live “Notebook” environment. It’s one of the best ways to learn practical skills like Python, Pandas, and Intro to Machine Learning.
Use Case Example: A student has learned the theory of machine learning and now wants to build their first model. They take the “Intro to Machine Learning” course on Kaggle and, within a few hours, have written Python code to train a real predictive model on a dataset.
Pricing Details: Completely Free. All courses, the notebook environment (including free GPU access), and the certificates of completion are 100% free.
What You’ll Need: A web browser.
Learning Curve: Intermediate. While the courses are beginner-friendly, they do require you to start writing code immediately, so some basic programming knowledge is helpful.
What to Watch Out For: Kaggle is focused on data science and machine learning. It’s not the best place to learn about general web development or software engineering.
Beginner Tutorial: The best way to start is with their “Python” course. You can find a great community-made walkthrough of the platform here on YouTube.
5. DeepLearning.AI
Best for: Learning from one of the most respected names in AI, Andrew Ng.
Founded by AI pioneer Andrew Ng, DeepLearning.AI creates some of the most popular and respected AI courses in the world. While many of their advanced specializations are paid, they offer several high-quality introductory courses for free (in audit mode on Coursera) and short, practical courses on their own platform that are perfect for students.
Use Case Example: A student wants to learn how to use AI models like ChatGPT more effectively. They take the “ChatGPT Prompt Engineering for Developers” course and learn specific techniques to write better prompts for their coding and writing projects.
Pricing Details: Free Course Access. Many of their most popular courses, like “AI for Everyone” and “Generative AI for Everyone,” can be audited for free on Coursera. They also offer some free short courses on their website.
What You’ll Need: A web browser.
Learning Curve: Beginner to Developer. They offer courses for all skill levels, from non-technical introductions to advanced developer-focused content.
What to Watch Out For: For the more advanced, multi-course “Specializations,” you will need a paid subscription to get the certificate.
Beginner Tutorial: The best place to start is with their famous “AI for Everyone” course. You can find an overview here on YouTube.
6. Hugging Face Courses
Best for: Learning Natural Language Processing (NLP) with state-of-the-art tools.
Hugging Face is the most important company and platform in the world of open-source Natural Language Processing (NLP). It’s like the GitHub for AI models. They also offer a completely free, comprehensive course that teaches you how to use their “Transformers” library, which is the industry standard for working with models like BERT and GPT.
Use Case Example: A computer science student wants to build a project that can analyze the sentiment (positive or negative) of customer reviews. They take the Hugging Face course to learn how to use a pre-trained model to build their sentiment analysis tool.
Pricing Details: Completely Free. The entire course, including all text, videos, and code examples, is free and open-source.
What You’ll Need: A web browser and a good understanding of Python.
Learning Curve: Developer. This course is designed for people who are already comfortable with Python and want to dive deep into the technical side of NLP.
What to Watch Out For: This is a highly technical, code-intensive course. It is not suitable for beginners with no programming experience.
Beginner Tutorial: The course is self-contained, but you can find a great overview of the Hugging Face platform here on YouTube.
7. freeCodeCamp
Best for: In-depth, university-level video courses on YouTube for free.
freeCodeCamp is a non-profit organization famous for its massive, high-quality programming tutorials. Their YouTube channel is a treasure trove of full-length courses on AI and machine learning, often several hours long. These are not just quick tutorials; they are comprehensive courses that cover theory and practical application, taught by university professors and industry experts.
Use Case Example: A student wants to learn everything about Python for machine learning. They watch freeCodeCamp’s “Python for Machine Learning” course on YouTube, a 12-hour, in-depth video that covers all the essential libraries and concepts.
Pricing Details: Completely Free. As a non-profit, all of their content on YouTube and their website is 100% free.
What You’ll Need: A web browser (to access YouTube).
Learning Curve: Beginner to Developer. They have content for all skill levels, from absolute beginner introductions to advanced deep learning courses.
What to Watch Out For: The courses are long-form videos, so they require a significant time commitment and self-discipline to complete.
Beginner Tutorial: A great starting point is their “AI for Beginners” course here on YouTube.
8. fast.ai
Best for: A practical, code-first approach to learning deep learning.
fast.ai is a research lab and education provider that offers one of the most famous and practical deep learning courses in the world. Their philosophy is “code-first,” meaning they teach you how to build and train state-of-the-art models right away, and then explain the theory behind them. It’s a top-down approach that is perfect for students who learn best by doing.
Use Case Example: A student who is bored with theory wants to build a real-world image classifier. In the first lesson of the fast.ai course, they learn how to build a model that can distinguish between different types of pets with over 99% accuracy.
Pricing Details: Completely Free. The entire course, including video lectures, online textbook, and code notebooks, is free with no ads.
What You’ll Need: A web browser and a good understanding of Python.
Learning Curve: Intermediate. The course is designed for people who already know how to code in Python.
What to Watch Out For: The course moves very quickly. It’s incredibly rewarding but requires a lot of effort to keep up.
Beginner Tutorial: The best way to start is with “Lesson 1” of their main course, which you can find here on their website.
9. Machine Learning Crash Course (Google)
Best for: A fast-paced, technical introduction to machine learning from Google’s experts.
This is Google’s own self-study course designed to give you a rapid introduction to the core concepts of machine learning. It’s a mix of video lectures, text explanations, and interactive visualizations that explain fundamental ideas like linear regression, neural networks, and training models. It’s more technical than the “AI Essentials” courses and is a great next step for students who want to go deeper.
Use Case Example: A student has a basic understanding of AI and now wants to learn the technical details of how a machine learning model is actually trained. They take the ML Crash Course to learn about concepts like “loss” and “gradient descent.”
Pricing Details: Completely Free. The entire course is free to access.
What You’ll Need: A web browser. Some pre-algebra and basic programming concepts are helpful.
Learning Curve: Intermediate. It’s designed to be a “crash course,” so it moves quickly through technical concepts.
What to Watch Out For: This is a self-study guide, not a full university course. It requires self-motivation to complete.
Beginner Tutorial: The course is self-contained. You can start with the first lesson, “Introduction to Machine Learning,” here.
10. Google AI Studio
Best for: Hands-on experimentation with Google’s latest Gemini AI models.
Google AI Studio is a browser-based playground for developers and curious students who want to experiment directly with Google’s powerful Gemini family of AI models. It’s not a course, but an interactive tool. You can craft prompts, test model responses, and then click “Get Code” to get the ready-to-use Python or JavaScript code to integrate those AI features into your own applications.
Use Case Example: A student wants to add a feature to their app that can summarize long articles. They use Google AI Studio to create and test a prompt that does this effectively, and then click “Get Code” to get the Python script they need to build the feature.
Pricing Details: Free to Use. Google AI Studio itself is free. When you use the models in your own app via the Gemini API, there is a very generous “free tier” with rate limits that are more than enough for student projects.
What You’ll Need: A web browser and a Google account.
Learning Curve: Developer. While the studio is easy to use, its purpose is to help you write code, so it’s intended for a technical audience.
What to Watch Out For: This is not a tool for building a full application; it’s a tool for building and testing the AI-powered parts of an application.
Beginner Tutorial: Google AI for Developers has a great official “Google AI Studio Quickstart” guide here on YouTube.
Which Resource Should You Start With?
Feeling a bit overwhelmed by the options? Here’s a quick guide:
If you’re an absolute beginner and want to know what AI is… start with Elements of AI or Google’s AI Essentials. They are the perfect non-technical starting points.
If you want to learn practical data science skills… go straight to Kaggle Learn. Their interactive courses are the best way to learn by doing.
If you know how to code and want to learn deep learning… your first stop should be fast.ai. Its practical, code-first approach is incredibly effective.
If you just want to get better at prompting… audit one of the Coursera prompt engineering courses.
Your Journey into AI Starts Now
As you can see, you have access to a world-class education in AI and machine learning without needing to spend a single rupee. From foundational concepts to advanced, hands-on coding, these free resources provide a clear path for any student to build the skills that are shaping the future.
The best way to learn is by starting. Pick one resource from this list that excites you the most, whether it’s a short introductory course or a hands-on coding tutorial, and dive in. You’ll be amazed at how much you can learn and build.
Written By
Prateek Singh.
Last Updated – August, 2025
About The Author
Prateek Singh believes the best way to learn is to get your hands dirty. He went from talking to customers in sales to building the online platforms they use. IndiaShouldKnow.com is his way of sharing that practical, hands-on knowledge with you.
FAQs About AI Use.
Can I trust every answer an AI tool gives me for my studies?
A: No, you should not trust every answer completely. Think of an AI as a super-smart assistant that has read most of the internet—but not every book in the library is accurate.
AI can sometimes make mistakes, misunderstand your question, or use outdated information.
It can even “hallucinate,” which means it confidently makes up an answer that sounds real but is completely false.
Rule of Thumb: Use AI answers as a great starting point, but never as the final, absolute truth. Always double-check important facts.
How can I verify the information I get from an AI for my academic work?
A: Verifying information is a crucial skill. It’s like being a detective for facts. Here are four simple steps:
Check Your Course Material: Is the AI’s answer consistent with what your textbook, lecture notes, or professor says? This is your most reliable source.
Look for Reputable Sources: Ask the AI for its sources or search for the information online. Look for links from universities (.edu), government sites (.gov), respected news organizations, or published academic journals.
Cross-Reference: Ask a different AI the same question, or type your question into a standard search engine like Google. If multiple reliable sources give the same answer, it’s more likely to be correct.
Use Common Sense: If an answer seems too perfect, too strange, or too good to be true, be extra skeptical and investigate it further.
What is the difference between using AI for research and using it to plagiarize?
A: This is a very important difference. It’s all about who is doing the thinking.
Using AI for Research (Good ✅):
Brainstorming topics for a paper.
Asking for a simple explanation of a complex theory.
Finding keywords to use in your library search.
Getting feedback on your grammar and sentence structure.
You are using AI as a tool to help you think and write better.
Using AI to Plagiarize (Bad ❌):
Copying and pasting an AI-generated answer directly into your assignment.
Asking the AI to write an entire essay or paragraph for you.
Slightly rephrasing an AI’s answer and submitting it as your own original thought.
You are letting the AI do the thinking and work for you.
How can I use AI ethically to support my learning without violating my school's academic honesty policy?
A: Using AI ethically means using it to learn, not to cheat. Here’s how:
Know the Rules: First and foremost, read your school’s or professor’s policy on using AI tools. This is the most important step.
Be the Author: The final work you submit must be yours. Your ideas, your structure, and your arguments. Use AI as a guide, not the writer.
Do the Heavy Lifting: Use AI to understand a topic, but then close the chat and write your summary or solve the problem yourself to make sure you have actually learned it.
Be Transparent: If you used an AI in a significant way (like for brainstorming), ask your professor if you should mention it. Honesty is always the best policy.
Can an AI's answer be biased? How can I detect this in its responses?
A: Yes, an AI’s answer can definitely be biased. Since AI learns from the vast amount of text on the internet written by humans, it can pick up and repeat human biases.
Here’s how to spot potential bias:
Look for Opinions: Does the answer present a strong opinion as a fact?
Check for One-Sidedness: On a topic with multiple viewpoints (like politics or economics), does the AI only show one side of the argument?
Watch for Stereotypes: Does the answer use generalizations about groups of people based on their race, gender, nationality, or other characteristics?
To avoid being misled by bias, always try to get information from multiple, varied sources.
Is it safe to upload my personal notes, research papers, or assignments to an AI tool?
A: It is best to be very careful. You should not consider your conversations with most public AI tools to be private.
Many AI companies use your conversations to train their systems, which means employees or contractors might read them.
There is always a risk of data breaches or leaks.
A Simple Safety Rule: Do not upload or paste any sensitive information that you would not want a stranger to see. This includes:
Personal identification details.
Confidential research or unpublished papers.
Your school assignments before you submit them.
Any financial or private data.
Related Articles
Sign Up for Our Newsletter To Learn More About the Latest In AI And Learn How To Use It.
Unlock your learning potential and stay ahead in the age of AI! Join the IndiaShouldKnow.com newsletter to discover how to seamlessly integrate Google AI into your studies for school, entrance exams, and college. Plus, get the latest insights on cutting-edge AI tools that can empower your career and enrich your life. Subscribe now for monthly updates.