If you’ve ever typed "Do I need to learn math for AI?" into Google and immediately closed the tab when you saw scary words like “Jacobian” or “Eigenvectors” — trust me, I’ve been there.

In fact, I avoided math for over 15 years.

But when I restarted my journey into AI, I realized that math — taught the right way — isn’t scary. It’s actually empowering.

This post is your friendly, practical guide to learning math for AI without going back to high school mode. These are the tools, resources, and personal hacks that helped me — someone who once feared probability — build real confidence in math again.

✅ The Best Free (and Useful) Resources to Learn Math for AI

Apart from my Masters course content, here are the free resources I either used or trust deeply for their clarity:

🧮 1. Khan Academy

This was recommended in my Masters program, and for good reason. The clean animations and logical progressions helped refresh long-forgotten topics like gradients, chain rule, and linear algebra.

🔗 https://www.khanacademy.org/math

📺 2. YouTube Channels

• 3Blue1Brown – Gorgeous visual explanations of complex math. Watch their series on linear algebra and calculus.

• StatQuest with Josh Starmer – Simplifies probability, stats, and ML topics with clean analogies.

• Welch Labs – Their "Neural Networks Demystified" series is gold.

• freeCodeCamp – Their long-form crash courses are solid.

📘 3. freeCodeCamp – Math for Machine Learning

A 6-hour course that’s perfect if you want to understand the math and see some coding too.

🔗 https://www.youtube.com/watch?v=aircAruvnKk

📊 4. Cheat Sheets and GitHub Repos

• Stanford’s CS229 Notes (Math-heavy, but legendary)

• Math for ML GitHub Repo: Summaries of key concepts with Python examples

🔗 https://github.com/llSourcell/Math-for-Data-Science

🛠 Tools That Helped Me Learn (and What I Plan to Use)

I didn’t start with a bunch of fancy math tools.

Instead, my learning looked like this:

• ✅ Python + Jupyter Notebooks — using NumPy, Pandas, Matplotlib, and Seaborn to visualize concepts

• ✅ Excel — still a favorite! I used it to visualize trends in gym attendance and model optimization problems

• 🤖 ChatGPT — Whenever I got stuck, I’d just ask. And it would explain math like a calm teacher.

🧪 I haven’t used Desmos or GeoGebra yet, but they’re now on my radar. If you’re a visual learner, those are worth checking out.

💡 My Personal Learning Hack

There’s just one trick that changed the game for me:

Make it real.

Until I find a real-world example — whether it’s gym scheduling, revenue predictions, or YouTube content planning — I struggle to internalize the math.

Once I do?

It sticks.

That’s why, throughout this blog series, I’ve used analogies from:

• 🏋️‍♂️ My gym business (peak hours, trainer optimization)

• 📈 YouTube growth strategies (expected value, binomial probabilities)

• 💼 My startup experience (resource allocation and trade-offs)

✉️ One Message to You (If You’re Scared of Math)

If the math is what’s stopping you from diving into AI, let me say this:

“Use AI to learn math — and then use math and AI to build something amazing.”

We’re not stuck in a classroom anymore. We’re building things. Automating things. Solving problems.

And AI is living proof that math isn’t boring or scary — it’s beautiful.

🧠 What’s Next?

This post wraps up my 7-part blog series — Math That Makes AI Work (For You).

If you’ve read this far, I hope you walk away believing:

• You don’t need to be a math wizard.

• You don’t need to memorize formulas.

• You just need the right mindset, the right resources, and the willingness to practice.

And if I — a mid-career engineer with no Python background — can fall in love with math again, so can you.

See you in the next series.
– BitByBharat