Math That Makes AI Work (For You): My Comeback Story Starts Here

Let’s be honest — when I first opened the Advanced Math & Programming for Data Science module in my AI/ML Masters program, I felt overwhelmed.

The syllabus looked long. The deadlines were short. And the flashbacks came fast — to high school probability, derivatives, and matrices. Subjects I had studied years ago… and then buried deep under decades of IT projects, job switches, and startup chaos.

My first thought?
“Why the hell are they making us go through this again?”

I’m a 45-year-old techie. I’ve worked across the US, UK, and India, for Fortune 500 companies. I’ve launched startups. I’ve built a gym. And now, I'm trying to rebuild myself in the world of AI. The last thing I expected was to be wrestling with topics like binomial distributions and Jacobian matrices.

But here’s what happened next.

The Shift That Changed Everything

When I stopped resisting and started learning — I realized something surprising:

💡 These concepts aren’t as complicated as I remembered. In fact, they’re beautifully logical.

Take probability, for example. I used to hate it. But when I revisited it after years — the fog lifted. I finally understood things like empirical probability. I could clearly see how it connects to machine learning models, how randomness is measured, and why distributions matter.

It wasn’t about solving equations.

It was about understanding how decisions get made — in AI systems, in startups, even in life.

Why This Series Matters (Especially If You’ve Been Away from Math)

This blog is the first post in a series I’m calling:

“Math That Makes AI Work (For You)”

Here’s what you can expect:

• Real-life analogies: Think of partial derivatives like understanding which gym exercise affects your weight loss the most.

• Foundational concepts: Like linear transformations, which help you grasp how AI reshapes data to make sense of it.

• Emotional storytelling: Because let’s face it — relearning math in your 40s isn’t just about logic, it’s about courage.

What’s Coming Up

I’ll break down topics like:

• Probability Distributions — How AI makes decisions in uncertain situations.

• Linear Algebra & Transformations — The secret behind computer vision, embeddings, and vector spaces.

• Multivariable Calculus — Gradients, optimization, and why AI models improve over time.

• And more — All explained in a way that feels like you’re learning with a friend, not a textbook.

For the Mid-Career Techie Who Feels Left Behind

If you're from my tribe — someone who’s spent 15+ years in IT and is now trying to catch up with the AI wave — I see you.

You're not late. You're just early in your next chapter.

And no — you don’t need to be a math wizard.
But yes — you do need to understand the math that makes AI work.

Because this isn’t just about models.
It’s about reclaiming your relevance — one concept at a time.

Let’s Learn, Rebuild, and Rise — Together

This is post #1 in the series.

If you’ve ever felt too old, too late, or too far gone to learn AI — this series is for you.

Let’s make advanced math feel simple, useful, and deeply human again.

✍️ Follow the full series at bitbybharat.com
🔖 Category: AI Learning Logs
🧵 Next Post: What Math You Actually Need for AI/ML (And What You Don’t)