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# Day 47 Of ML grind: Guess what's this ??? - this is the 3d view of mnist dataset and right now working on coding part of PCA - understood the geometric intuition and formulation - what's eigen values & eigen vectors - next starting with k-means clustering What's ur cover up ??

Almost done with all the Supervised Learning algorithms — only Naive Bayes remains 🍃 Balancing exam prep and my daily ML grind ☕ Exams are on, but the grind never stops 🔥 What u ml guys think covering the naive bayes is imp or starting Unsupervised learning??

How patiently, for the last 20 minutes I am waiting to complete this code running

Yo chat ✌🏻 that's Day 48 of my ml grind: for today I have covered up: - Full Naive Bayes - from probabilistic math to intuition and problem solving - did sentiment analysis on IMDB dataset - it took too much time to complete and here my Supervised Learning ends 🔥 Your grind ??

Yup started with the K-Means Clustering done with the Core intuition but now some rest 😮‍💨

Hey there everyone I have almost completed my Level 1 and half of Level 2 and I think till nov I will complete it.... due to clg exams there is a lil pressure and will soon enter Level 3 ready to grind 🔥

# Day 49 Of ML: - done with the k-means clustering - wrote code on a sample dataset - wrote code from scratch for k-means clustering

heyy chat completed the k-means clustering from scratch and just left with the WCSS score will implement it after a short break Happy coding :)

Rohit 121(125) Virat 74(81) Finally relief 😌

50th day of the ML, - covered up the Agglomerative & DBSCAN clustering - understood the geometric intuition & algorithms - quite good to learn & implemented on the iris & credit card datasets - good intuition with dendograms and 3d visualizations Your grind for today??

# Day 51 Of ML, so chat today I read the homl book on dimensionality reduction, understood some feature scaling concepts, then standardization & normalisation. Also studied SHAP & summary & dependence plots. Also left with some pages in the unsupervised learning part. Happy :).

Yo✌🏻 For the last 52 Days here is what I have covered: - read 360+ pages of Homl book - covered 17 algorithms in core ML - understood different topics in ml - worked on different datasets and small notebooks - will do 2 to 3 ml projects & then DL after my sem exams What about u?

waiting for my sem exams to end so that i can start DL

Locked in on a small dataset in the night

Hello, chat ✌🏻 - Just completed my first ML project - Detailed End-to-End project on Telco-customer-churn.csv dataset github.com/Om-codex/Telco… Check out the live App on render : …hurn-prediction-om-codex.onrender.com

Hello chat 👋🏻, here I have provided my own ML handwritten notes if you think it's useful for you plz, feel free to explore my notes Basic of ML :- drive.google.com/file/d/1QGE_Xi… ML Algorithms :- drive.google.com/file/d/1_d9k4E…

Hello chat 👋🏻, here I have provided my own ML handwritten notes if you think it's useful for you plz, feel free to explore my notes Basic of ML :- drive.google.com/file/d/1QGE_Xi… ML Algorithms :- drive.google.com/file/d/1_d9k4E…

Hey 👋🏻 chat, Just deployed my static webpage built using my learnings + HTML, CSS, JS + ChatGPT! It visualizes neural networks — from perceptrons to backpropagation, with smooth animations ⚡ 🌐 Live on Netlify → neural-nets.netlify.app Transforming learning into visuals 🎨

Hey 👋🏻 chat, Just deployed my static webpage built using my learnings + HTML, CSS, JS + ChatGPT! It visualizes neural networks — from perceptrons to backpropagation, with smooth animations ⚡ 🌐 Live on Netlify → neural-nets.netlify.app Transforming learning into visuals 🎨