Let me be honest.

Despite two decades in tech, I never touched Python.

I built enterprise software in COBOL, JCL, Java, .NET, C++, and even Node.js. But Python? It never felt necessary… until I enrolled in a Master’s in AI/ML and Data Science earlier this year.

The first lesson?

“Learn Python. Or you’re going to struggle.”

That hit me.

Why this language? Why now? What made Python the de facto choice for AI, machine learning, and data science?

Let’s break it down — not as a tutorial, but as a beginner’s reality check.

The Setup: Old Languages, New Problems

For years, tech was about writing performant backend systems, building UIs, or managing infrastructure. Languages like Java or C++ were king.

But AI/ML is a different beast.

You're no longer just coding logic — you're processing massive datasets, training models, and visualizing insights. You need a language that speaks both to humans and machines.

Python is that language.

Why Python Works So Well (Especially for AI/ML)

Here’s what I’ve learned from both my course and my own coding sessions:

1. Simple, Clean Syntax

• Feels like writing English.

• You spend less time worrying about boilerplate, more time experimenting.

2. Massive Library Support

• Want to clean and process data? Use pandas.

• Crunch math at scale? Use NumPy.

• Build a model? Scikit-learn, TensorFlow, or PyTorch have you covered.

• Want to visualize trends? Matplotlib and Seaborn make it beautiful.

3. Huge Community & Ecosystem

• If you're stuck, someone’s already solved it.

• Tons of tutorials, forums, and open-source code.

4. Cross-Industry Adoption

• From healthcare to finance to marketing — Python dominates.

But It’s Not Perfect (And That’s Okay)

Let’s be real. Python has downsides too:

• Slower than compiled languages like C++ or Java.

• Not great for mobile development (almost nobody uses it there).

• Whitespace sensitivity can be annoying if you’re coming from bracket-heavy languages.

But here’s the thing…

In AI/ML, you’re not optimizing for speed — you’re optimizing for experimentation, iteration, and results. And for that, Python is unbeatable.

My Experience So Far

I started with a Python Bootcamp. Having coded before, it felt easy to pick up — but the real magic was discovering how fast I could build things.

For example:

• I processed 1M+ rows of data with pandas.

• Created my first data visualizations in under 10 minutes.

• Built a simple regression model in just a few lines of code.

I’m still learning — and I plan to share every tool, use case, and notebook here at BitByBharat.

Key Takeaway

If you’re just starting in AI/ML or data science, don’t overthink it.

✅ Start with Python.
✅ Stick with it for 90 days.
✅ Build tiny projects as you go.
✅ Let the ecosystem do the heavy lifting.

This language isn’t just a tool. It’s a launchpad.

Want to Follow My Learning Journey?

I’m documenting everything I learn in real time — the wins, the mess-ups, and the workflows that actually work.

👉 Bookmark BitByBharat.com
👉 DM me if you’re stuck or starting
👉 Drop a 🔥 in the comments if you want my beginner-friendly Python + AI learning path

Let’s rebuild — one line of code at a time.