Yash Dadlani

DSA | ML | Running on black coffee & tweeting in O(1) time complexity ☕⌨️

[Yash Dadlani @YashDadlani04]

Day 7/n of my ML journey:

✅ Gym in the morning(then clg again)
✅Practised maths particularly statistics and LA.
✅Also watched LA lectures of 3b1b(That's goated one)

Will grinder harder this weekend.TBC:))

[Yash Dadlani @YashDadlani04]

Day 6/n of my ML journey:

✅ 90-min gym session
Clg again so had to deal with it:(
✅Did some maths(Linear Algebra)today and revised some SQL topics

TBC))

[Shoaib Khan @fdjpedri]

Messi FIFA Word Cup 2026 🪄

[Yash Dadlani @YashDadlani04]

@qazisays Wtff😭😭

[sankalp @dejavucoder]

claude code is having it's cursor moment after karpathy sensei's post. never been a better time to try it.

my latest blog on how to get the most out of claude code 2.0 and other agents in general is up now. grab a chai and have fun reading!

https://t.co/cQpvo0xocY

[Resko★ @Bloke_Baz]

lowkey afraid of the amount of work i actually need to do next year in pursuing my dreams.

[Yash Dadlani @YashDadlani04]

@Om_Codes_ Congrats Mann🫂

[Yash Dadlani @YashDadlani04]

@Om_Codes_ Good luck mate!!

[Yash Dadlani @YashDadlani04]

@just_avik Bookmarked for educational purpose😭

[Yash Dadlani @YashDadlani04]

@udayempire_ True

[Yash Dadlani @YashDadlani04]

@lochan_twt 😭😭

[Yash Dadlani @YashDadlani04]

@Pseudo_Sid26 True

[Yash Dadlani @YashDadlani04]

@nothiingf4 Lets connect!!!

[Yash Dadlani @YashDadlani04]

@nothiingf4 Nice one brother

[Yash Dadlani @YashDadlani04]

@nothiingf4 True!!

[Ajibola @4jibola]

the amount of locking in and discipline it takes to change your life is crazy.

[Dhruv @dhruvtwt_]

Guysssss, I've completed making the Srimad Bhagavad Gita Dataset, do use it for making something beneficial for the mankind and suggest me any ideas that can be implemented and tell me if you find any mistake I might have made.

Om Namo Bhagavate Vasudevaya 🙏🙏

[Yash Dadlani @YashDadlani04]

@wizard_32 Hi

[vixhaℓ @TheVixhal]

Today, the warden of the girls hostel suddenly came into my girlfriend's room… and the worst part was that I was in my girlfriend's room 😭.

Now imagine the scene:
- Warden banging on the door.
- My girlfriend panicking.

Me standing there like, "Bro, this is how my college journey ends."

The warden starts interrogating me:
- What are you doing here?
- Where is your ID card?
- Are you really her 'cousin'?

Each question felt like a mini-death penalty. I knew one wrong answer and my entire semester GPA would be replaced by an FIR number.

And that's basically what Bayes Theorem does: It's the warden of probability: interrogating our assumptions with evidence, and updating beliefs step by step.

Bayes Theorem is nothing but just a mathematical way to update your beliefs when you see new evidence.

Formula:
P(H | E) = (P(E | H) × P(H)) / P(E)

Where:
- H: Hypothesis
- E: Event (what you observe)
- P(H): Prior probability
- P(E): Overall probability of event
- P(E|H): Prob. of event if hypothesis was true
- P(H|E): Probability hypothesis is true given event (updated belief)

Let's take an example:

In a neighborhood, 90% of children were falling sick due to flu and 10% due to measles (no other diseases). The probability of observing rashes for measles is 0.95 and for flu is 0.08. If a child develops rashes, find the probability that the child has flu.

Let's solve step by step:

Step 1: Define Hypotheses
- H1: The child has flu
- H2: The child has measles

Step 2: Define Event
- Event (E) = Child has rashes

Step 3: Write Priors
- P(H1) = 0.9 (90% children have flu)
- P(H2) = 0.1 (10% children have measles)

Step 4: Calculate Likelihoods
- P(E|H1) = 0.08 (rash probability if flu)
- P(E|H2) = 0.95 (rash probability if measles)

Step 5: Calculate Total Probability of Rashes
- By law of total probability:
- P(E) = P(E|H1)•P(H1) + P(E|H2)•P(H2)
- P(E) = (0.08)•(0.9) + (0.95)•(0.1)
- P(E) = 0.072 + 0.095
- P(E) = 0.167

So overall, 16.7% of children develop rashes.

Step 6: Apply Bayes Theorem
- P(H1 | E) = (P(E | H1) × P(H1)) / P(E)
- P(H1 | E) = ((0.08) × (0.9)) / 0.167
- P(H1 | E) = 0.431

Final Answer:

If a child has rashes, the probability they have flu = 43.1%.

Congratulations 🎉, you've just learned Bayes Theorem!

Bonus: Applications of Bayes Theorem in AI/ML

1. Recommendation Systems:
Netflix doesn't just recommend based on genre. It uses Bayes Theorem: "Given that this person watched 5 horror movies, what's the probability they'll like this thriller?" It updates recommendations as you watch more content.

2. Naive Bayes Classifier:
One of the easiest yet surprisingly powerful ML algorithms. It assumes features are independent (naive assumption) and uses Bayes Theorem to classify things like:
- Spam vs. Ham emails
- Sentiment analysis
- Document categorization

3. Advanced Applications:
Once you understand the basics, Bayes is behind many advanced techniques:

Hidden Markov Models (HMMs): For speech recognition, part-of-speech tagging

Expectation-Maximization (EM): For Gaussian Mixture Models, handling missing data

Bayesian Optimization: Efficient hyperparameter tuning for ML models

Bayes Theorem is the mathematical foundation for handling uncertainty in machine learning. Every time an algorithm needs to update its beliefs based on new evidence, Bayes Theorem is working behind the scenes!

[Yash Dadlani @YashDadlani04]

@TheVixhal Deserves a RT

[Yash Dadlani @YashDadlani04]

@sejalnamdev_ Nice:))