Deepika

Learning AI & ML
LLM | NLP | ML Fundamentals
Building real projects with FastAPI
Hashnode Blogger ✍️

[Deepika @Deepika_0803]

Hey! I'm Deepika 👋

Recent CSE graduate learning AI/ML in public. Currently exploring NLP, sharing my learnings on Hashnode, and building real-world projects.

Let's connect and learn together!

#NLP #MachineLearning

[Deepika @Deepika_0803]

Small win today: understood LangGraph persistence

It’s basically state save + restore
Which explains how chats magically resume

Also did EDA on a dataset…
Every month has all 4 seasons

Real world data = creative writing?
Or is this normal?

[Deepika @Deepika_0803]

LangGraph is making agents feel less like code, more like systems.

Built a small chatbot today
Then went dataset hunting for XGBoost

Funny part?
Finding the right dataset > building the model

Lowkey want a “describe → get datasets” tool

[Deepika @Deepika_0803]

Got a Prompt Engineer interview tomorrow.
Rounds include English test, aptitude, coding, GD…
but nothing that evaluates prompting.
Offer: 3 LPA + 2-year bond.
Still attending though. Interviews are just another dataset for learning.

[Deepika @Deepika_0803]

Today I learned XGBoost.

Gradient Boosting but with:
⚡ speed
📉 regularization
🧠 smart optimizations

Ran a small regression example. Tomorrow: real dataset.
Starting to understand why everyone in ML interviews keeps asking about this.

Respect to Tianqi Chen

[Deepika @Deepika_0803]

Tried running Aden Hive today.

Mistake #1: didn’t read the docs.
Mistake #2: assumed it would “just work”.

Eventually got it running on bash… but it quietly switched to an Anthropic model instead of the one I configured.

AI agents are autonomous… apparently configs are too.

[Deepika @Deepika_0803]

Learned Gradient Boosting for classification today. Same story as regression… just a different loss function. Also discovered ML involves more formulas than my memory likes.
Took a break and tried some random photography too.

[Deepika @Deepika_0803]

Today I studied the math behind Gradient Boosting.

First 10 minutes: “This is easy.”
Then came additive modeling…
Then gradients…
Now I’m confused but also curious.

I guess solving problems is the only way forward.
How did you guys actually learn this?

[Deepika @Deepika_0803]

Spent today learning Random Forest.
Thought it was a completely different algorithm… turns out it’s just many decision trees trained on random data.
My Sunday ended with a realization (I’m dramatic). How’s yours?

[Deepika @Deepika_0803]

Took an AI hiring challenge.

Job title: AI Engineer
Questions:
• ML
• NLP
• Coding

LLMs, agents, RAG… nowhere to be found.

At this point I’m convinced
AI interviews are just ML interviews in disguise.

[Deepika @Deepika_0803]

For people running AI agencies:

How do you figure out which companies might actually need your solution?

Manual research? Signals you watch? Just experience?

Trying to learn how agencies identify potential clients.

[Deepika @Deepika_0803]

Building in AI feels strange sometimes.

There’s always something new:
new models
new frameworks
new terms

Today I didn’t build anything.
Just thinking, learning, processing.

Tomorrow we build again.

[Deepika @Deepika_0803]

Today: built a LangGraph agent with self-evaluation.
Also today: discovered 5 new LLMs, 3 renamed ones, and 2 “state-of-the-art” replacements.
Building agents is easier than tracking model names.

[Deepika @Deepika_0803]

Worked on parallel workflows in LangGraph + built an IELTS Task 2 evaluator. Still haven’t locked in my March goals.
Moving forward, figuring it out as I go.

[Deepika @Deepika_0803]

Built an Employee Attrition Prediction system.
Model + SHAP interpretability + FastAPI + Gradio + Docker.
Production-ready setup.
GitHub: https://t.co/hs7cgEcxoY

#LearningInPublic #MachineLearning

[Deepika @Deepika_0803]

February so far: 2 projects done. Sentiment analysis, and a full employee attrition pipeline(EDA -> Deployment). Learned a lot, had fun, and honestly… it felt more like solving a mystery than coding ML.

[Deepika @Deepika_0803]

Spent the day connecting FastAPI backend to a Gradio frontend for my attrition model.
Prediction + probability + SHAP explanations working end-to-end. Feels good to see it live.

[Deepika @Deepika_0803]

ML + SHAP = magic✨
My attrition model now tells not just who leaves, but why.
App’s backend & frontend are next.

What did you work on today?

[Deepika @Deepika_0803]

@matthewncollins True.
And just like TCP/IP, most people use it without understanding how powerful it actually is.

[Deepika @Deepika_0803]

Big realization today:
Sklearn can build the model.
But only you can understand the data.
Spent more time on EDA → got better results.

ML isn’t model-first. It’s data-first.

[Deepika @Deepika_0803]

@0xAl3x_ Cool, I’m excited to see your mentor chatbot too.