Hey everyone! 👋
I'm Nathishwar, an engineering student and an aspiring AI/ML engineer. Like many of you, I started my journey in Artificial Intelligence filled with excitement... and confusion. 😅
Back when I decided to dive into AI, I didn't have the budget for fancy paid courses or expensive bootcamps. I had just my laptop, internet, and a strong curiosity to figure out how machines can actually think. And guess what? That was more than enough.
In this post, I want to share the top free resources that genuinely helped me learn AI—from zero to building real projects. These aren't just random links; these are battle-tested, coffee-powered, "this-is-how-I-actually-learned" resources. Let's go!
📺 YouTube – My AI Classroom
Let's be real: YouTube was my first AI professor.
🔹 Top channels that helped me:
- • freeCodeCamp.org – Amazing comprehensive courses for free
- • StatQuest with Josh Starmer – Simplified even the scariest ML math
- • Sentdex – Great practical Python and ML tutorials
- • 3Blue1Brown – Made neural networks click in my brain like never before
🧠 Why it worked:
YouTube helped me get the intuition behind complex concepts before I even touched the code. And when you're just starting out, understanding the "why" is just as important as the "how."
🔍 Google's Machine Learning Crash Course
This is one of the most beginner-friendly and professionally made resources. It walks you through ML concepts using interactive lessons and coding exercises in TensorFlow.
🔹 What you'll find:
🔗 https://developers.google.com/machine-learning/crash-course
A structured path through fundamental ML concepts with interactive visualizations and hands-on TensorFlow exercises.
👉 My tip:
Don't rush it. Watch the video, try the colab notebooks, and take notes. You'll thank yourself later.
🏆 Kaggle – Learn and Practice Together
Kaggle is more than just competitions. I used its "Learn" micro-courses to pick up essential skills.
🔹 What I learned there:
🔗 https://www.kaggle.com/learn
- • Python
- • Pandas
- • Data Visualization
- • Intro to Machine Learning
- • Deep Learning with PyTorch and TensorFlow
💡 Why it's effective:
And then—BAM!—I tried their real datasets to build projects. It helped me bridge theory with real-world application.
🎓 Coursera (Free Audits Only!)
Okay, I know I said no paid courses—but Coursera allows you to audit most courses for free (no certificate though).
🔹 My personal favorites:
- • AI for Everyone by Andrew Ng – Non-technical but inspiring!
- • Machine Learning by Andrew Ng – The OG classic
👉 Pro Tip:
Choose "Audit" when enrolling. You'll get full access to videos and reading material.
💻 GitHub Repos & Open-Source Projects
I discovered some amazing AI/ML projects and roadmaps through GitHub.
🔹 What helped me:
One gem that guided me early on was the AI Roadmap by AMAI – it's like a Google Maps for your AI learning path.
🚀 My journey:
Eventually, I started forking projects, experimenting with Jupyter notebooks, and even contributing to beginner-friendly open-source issues.
📚 Books and Blogs (Free PDFs & Medium Articles)
Not everything has to be a video. Sometimes reading helps more.
🔹 Free Books:
- • Dive into Deep Learning – Practical, code-first approach
- • The Hundred-Page Machine Learning Book (Unofficial PDF) – When I wanted a crash course
🔹 Blogs:
- • Towards Data Science (Medium)
- • Analytics Vidhya
📝 My process:
I bookmarked a ton of articles and turned them into weekend reading marathons.
👥 Reddit & Discord Communities
When I was stuck—or just felt overwhelmed—I turned to online communities.
🔹 My go-to communities:
- • r/MachineLearning
- • r/learnmachinelearning
- • AI/ML-focused Discord groups
🤗 The human factor:
Just talking to others learning the same stuff made me feel less alone. Struggles are real, but when someone says, "Oh I got stuck there too!", it motivates you to keep going.
☁️ Google Colab – My Coding Playground
I didn't even have a powerful laptop. But Google Colab saved me.
🔹 Why it's amazing:
It let me run TensorFlow, PyTorch, and heavy data tasks on the cloud for free. No setup. Just code and go.
🔥 My milestone:
I built my first CNN model for hand gesture recognition on Colab. It felt magical.
💭 Final Thoughts
If you're someone who wants to learn AI but can't afford paid resources, I feel you. But here's the good news:
You don't need to spend a rupee to get started in AI. What you need is consistency, curiosity, and a community.
My journey is still ongoing, and I'm learning new things every day. But if my story proves anything—it's that free resources, when used right, can take you very far.
So pick one resource from the list above. Start today. And don't forget to enjoy the process. AI is tough, but it's also really fun when you see your first model work.
If you found this helpful, feel free to connect with me. Let's grow and build together! 🚀
- Nathishwar C
"The best resources for learning aren't always the ones with the highest price tag, but the ones that spark your curiosity and keep you coming back for more."
