Data Science with Keerthi (தமிழில்)

Data Scientist opportunity in big MNCs Amazon Microsoft Intuit LSEG PayPal Qualcomm


Intuit Hiring !!
Role - Senior AI Scientist
Exp - 1+ year
Link - lnkd.in/gVnRbBWc

PayPal Hiring !!
Role - Data Scientist
Exp - 1+ year
Link - lnkd.in/gRqxpqVg

Amazon Hiring !!
Role- Applied Scientist
Exp - 1+ year
Link - lnkd.in/g82-GfHF

MakeMyTrip Hiring !!
Role - Data Scientist 1
Exp - 2 year
Link - lnkd.in/gY56WJd7

Qualcomm Hiring !!
Role - AI Engineer
Exp - 2 year
Link - lnkd.in/g48gWpfG





For preparation checkout my YouTube channel - youtube.com/@ds_with_keerthi


#job #Hiring #datascientist #updates #Engineer

2 days ago | [YT] | 4

Data Science with Keerthi (தமிழில்)

Data Science Intern openings - Hyderabad

🚀 Deccan AI is hiring #interns 🚀



Deccan AI is looking for sharp and motivated interns to join our team for next-generation AI evaluation and alignment work.

If you have prior experience in AI data tasks, annotation, evaluation, or RLHF-style projects — this opportunity is for you!



🔹 Role: AI Data Consultant Intern / AI Evaluation Intern
🔹 Location: Hyderabad (In-office)
🔹 Stipend: ₹50,000 per month
🔹 Duration: 6 Months (January-June)
🔹 Requirement: Prior experience in similar projects/internships
🔹 Type: Full-time Internship (In office)



You will work on evaluating multi-modal AI outputs and play a direct role in improving how modern AI systems learn, reason, and respond.



📩 Interested candidates can apply at: - dhairya@deccan.ai



Placement Cells from colleges are also welcome to connect with us for collaboration and internship opportunities at the same email:-
dhairya@deccan.ai



Feel free to share or tag someone who might be a great fit!

#DeccanAI #Hiring #Internship #AIJobs #AIEvaluation #RLHF #DataConsultant #HyderabadJobs #CampusHiring #HiringInterns

3 days ago | [YT] | 6

Data Science with Keerthi (தமிழில்)

What Laundry Taught Me About 𝐍𝐚𝐢𝐯𝐞 𝐁𝐚𝐲𝐞𝐬 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫!
🎥: https://youtu.be/ueG87MuM0Tc



“𝑳𝒊𝒈𝒉𝒕 𝒄𝒐𝒕𝒕𝒐𝒏? → Machine wash.”
“𝑫𝒆𝒍𝒊𝒄𝒂𝒕𝒆 𝒅𝒂𝒓𝒌 𝒇𝒂𝒃𝒓𝒊𝒄? → Hand wash.”

And suddenly it hit me —
this is EXACTLY how the Naive Bayes classifier thinks. 😄



So I turned that everyday moment into a fun ML lesson!

👕 How simple features like fabric, color, and print help you “predict” wash type
🧠 How Naive Bayes uses 𝒑𝒓𝒊𝒐𝒓 + 𝒍𝒊𝒌𝒆𝒍𝒊𝒉𝒐𝒐𝒅 to make decisions
📊 Why 𝑩𝒂𝒚𝒆𝒔 𝒕𝒉𝒆𝒐𝒓𝒆𝒎 is just updating belief after seeing new evidence
🧁 And why the algorithm is still one of the 𝒔𝒘𝒆𝒆𝒕𝒆𝒔𝒕 & 𝒔𝒊𝒎𝒑𝒍𝒆𝒔𝒕 in ML



If you’ve ever felt ML is too math-heavy, this video will make Naive Bayes feel like doing laundry — easy, predictable, and oddly satisfying. 😅



#MachineLearning #DataScience #NaiveBayes #BayesTheorem #Probability #MLAlgorithms #AI #TechCommunity

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1 week ago | [YT] | 8

Data Science with Keerthi (தமிழில்)

New video alert — 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧: All in One!

Video - https://youtu.be/8FCbw0CgsWI?si=fs0T2...


Instead of overwhelming definitions, I focused on
what they are?
who invented them?
when to use them?
why they matter? and how to understand them intuitively.

🎲𝐃𝐢𝐬𝐜𝐫𝐞𝐭𝐞 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬 - (our yes/no world)
1. Bernoulli – basic “success or failure”
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘍𝘭𝘪𝘱 𝘢 𝘤𝘰𝘪𝘯 𝘰𝘯𝘤𝘦.

2. Binomial – multiple attempts of Bernoulli
𝘌𝘹𝘢𝘮𝘱𝘭𝘦:𝘍𝘭𝘪𝘱𝘱𝘪𝘯𝘨 𝘵𝘩𝘦 𝘤𝘰𝘪𝘯 10 𝘵𝘪𝘮𝘦𝘴.

3. Poisson – rare events in a fixed interval
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘈𝘤𝘤𝘪𝘥𝘦𝘯𝘵𝘴 𝘪𝘯 1 𝘩𝘰𝘶𝘳.

4. Geometric – how many tries until first success
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘏𝘰𝘸 𝘮𝘢𝘯𝘺 𝘧𝘭𝘪𝘱𝘴 𝘵𝘪𝘭𝘭 𝘧𝘪𝘳𝘴𝘵 𝘩𝘦𝘢𝘥.

📈 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬 (our natural world)
1. Uniform – Every value is equally likely
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘙𝘢𝘯𝘥𝘰𝘮 𝘯𝘶𝘮𝘣𝘦𝘳 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 0 𝘢𝘯𝘥 100

2. Normal Distribution – heights, weights… all the “bell curve” stuff
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘏𝘦𝘪𝘨𝘩𝘵𝘴 𝘰𝘧 𝘴𝘵𝘶𝘥𝘦𝘯𝘵𝘴.

3. Standard Normal – why we convert to Z and compare across groups
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘊𝘰𝘮𝘱𝘢𝘳𝘪𝘯𝘨 𝘮𝘢𝘳𝘬𝘴 𝘢𝘤𝘳𝘰𝘴𝘴 𝘤𝘭𝘢𝘴𝘴𝘦𝘴.

4. Log Normal – when things grow multiplicatively (salaries, stock prices)
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘚𝘢𝘭𝘢𝘳𝘪𝘦𝘴, 𝘴𝘵𝘰𝘤𝘬 𝘱𝘳𝘪𝘤𝘦𝘴.

5. Pareto / Power Law – the classic 80–20 world (few have a lot, many have little) 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘞𝘦𝘢𝘭𝘵𝘩 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯.



hashtag#Statistics hashtag#DataScience hashtag#Probability hashtag#Distributions hashtag#NormalDistribution hashtag#UniformDistribution hashtag#DataScienceWithKeerthi

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2 weeks ago | [YT] | 8

Data Science with Keerthi (தமிழில்)

New video alert — 𝑪𝒐𝒗𝒂𝒓𝒊𝒂𝒏𝒄𝒆 𝒗𝒔 𝑪𝒐𝒓𝒓𝒆𝒍𝒂𝒕𝒊𝒐𝒏: The Real Difference!



🍦 Ice Cream Sales vs Temperature 🌡️ — visualized, step-by-step to show:

✨ How 𝐜𝐨𝐯𝐚𝐫𝐢𝐚𝐧𝐜𝐞 just shows direction, not strength?
💥 Why outliers break 𝐏𝐞𝐚𝐫𝐬𝐨𝐧?
📈 How 𝐒𝐩𝐞𝐚𝐫𝐦𝐚𝐧 saves the day?



Video - https://www.youtube.com/watch?v=joVOM...



#MachineLearning #DataScience #Correlation #Covariance #DataScienceWithKeerthi #StatisticsForMachineLearning

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1 month ago | [YT] | 5

Data Science with Keerthi (தமிழில்)

New video in 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 மெதுவாக Series !


Ever tasted Machine Learning through sweets?
𝐗𝐆𝐁𝐨𝐨𝐬𝐭 𝐅𝐭.லட்டு 𝐯𝐬 ஜிலேபி 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 - https://youtu.be/nKzH3zu-xLY

✅ How Gradient Boosting learns from residuals?
✅ Why XGBoost introduces Hessians?
✅ How regression trees power classification?
✅ The real math behind those leaf weights?

PS: Every formula is explained the way Appa & Amma would understand ❤️
hashtag#MachineLearning hashtag#XGBoost hashtag#DataScience hashtag#LearninTamil hashtag#DecisionTrees

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1 month ago | [YT] | 5

Data Science with Keerthi (தமிழில்)

“𝐋 𝐟𝐨𝐫 𝐋𝐋𝐌” (Episode - 3)
𝑷𝒓𝒐𝒎𝒑𝒕 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 — The art of talking to LLMs!



LLMs are smart but they don’t read your mind. They read your INSTRUCTIONS. 🧩 It’s all in the prompt!


You don’t need a bigger model to get better response; you need a better conversation.



Every word in your question acts like a compass 🧭 guiding the LLM’s thought path.
💬 It’s 𝐧𝐨𝐭 𝐜𝐨𝐝𝐢𝐧𝐠 — 𝐢𝐭’𝐬 𝐜𝐨𝐚𝐜𝐡𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐀𝐈 to think the way you do.
Once you master it, you realize —
🧠 Prompting is the new programming language of the AI era!



#AI #ArtificialIntelligence #MachineLearning #DeepLearning #LLM #PromptEngineering #GenerativeAI #DataScience #LLMTraining

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1 month ago | [YT] | 5

Data Science with Keerthi (தமிழில்)

New video in 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 மெதுவாக Series !

🎯 New YouTube Video Drop — 𝐆𝐫𝐚𝐝𝐢𝐞𝐧𝐭 𝐁𝐨𝐨𝐬𝐭𝐢𝐧𝐠 (Ft. The AC Dataset)
📺 Watch full breakdown → https://youtu.be/UFMBXkB7BR0?si=bnyjy...

🔸 Gradient = derivative of loss

🔸 Each tree fits residuals from previous step

🔸 Regression → MSE loss

🔸 Classification → Log-loss & log-odds

🔸 Controlled updates using learning rate

🔸 Final prediction = base + sum of all tiny corrections

#GradientBoosting #DataScience #MachineLearning #DataScienceWithKeerthi #ArtificaialIntelligence

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1 month ago (edited) | [YT] | 10

Data Science with Keerthi (தமிழில்)

Introducing my new series: “𝐋 𝐟𝐨𝐫 𝐋𝐋𝐌”(Episode - 1):



𝑳𝒂𝒓𝒈𝒆 𝑳𝒂𝒏𝒈𝒖𝒂𝒈𝒆 𝑴𝒐𝒅𝒆𝒍
AI systems trained on massive amounts of text (books, articles, websites, conversations). LLMs are powerful pattern recognizers that feel intelligent because of scale.

Example : ChatGPT, Claude, and more......................................................................



But, How exactly are these modern LLMs trained?
1️⃣ 𝐏𝐫𝐞𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Model learns language patterns and knowledge from massive text datasets.
2️⃣ 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 (𝐒𝐅𝐓): Humans teach the model to follow instructions with curated examples.
3️⃣ 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Multiple model outputs are ranked to capture what humans actually prefer.
4️⃣ 𝐑𝐞𝐰𝐚𝐫𝐝 𝐌𝐨𝐝𝐞𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: A model is trained to score outputs based on human preferences.
5️⃣ 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐑𝐋𝐇𝐅/𝐃𝐏𝐎): The model is fine-tuned to maximize helpful, safe, and aligned.

Tada! ChatGPT is ready to be shipped! 🚀


As usual i have some handcrafted visualizations to make it easy for digestion. Go check out now----->



#AI #ArtificialIntelligence #MachineLearning #DeepLearning #LLM #ChatGPT #ClaudeAI #NaturalLanguageProcessing #NLP #AIResearch #TechInnovation #GenerativeAI #AIExplained #DataScience hashtag#AICommunity #RLHF #RLAIF #LLMTraining

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2 months ago | [YT] | 12

Data Science with Keerthi (தமிழில்)

New video in 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 மெதுவாக Series !


AdaBoost (Ft. Saravana Bhavan) - https://youtu.be/EliLxkKUDQ0




Here’s why it’s one of my favorite ML algorithms 👇
✅ 𝐃𝐨𝐞𝐬𝐧’𝐭 𝐝𝐢𝐬𝐜𝐚𝐫𝐝 𝐰𝐞𝐚𝐤 𝐦𝐨𝐝𝐞𝐥𝐬 – it makes them work harder.
✅ 𝐋𝐞𝐚𝐫𝐧𝐬 𝐟𝐫𝐨𝐦 𝐦𝐢𝐬𝐭𝐚𝐤𝐞𝐬 – misclassified points get more weight next round.
✅ 𝐓𝐞𝐚𝐦 𝐞𝐟𝐟𝐨𝐫𝐭 – many weak learners combine into one strong learner.
✅ 𝐒𝐢𝐦𝐩𝐥𝐞 𝐦𝐚𝐭𝐡, 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐢𝐦𝐩𝐚𝐜𝐭 – a great mix of theory + real-world use.

If you’ve ever wondered how machines get better by learning from errors, this is the algorithm to explore.



#MachineLearning #AI #Boosting #DataScience #AdaBoost

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2 months ago | [YT] | 13