Vishwajeet Padole @P__Vishwajeet
20. AI/ML I also Tweet About Football⚽ Maharashtra , India Joined January 2025-
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Day 9 of #ML --> Worked with the Breast Cancer Wisconsin dataset(569 samples,30 features) --> Learned about validation / train and test split & the holdout method --> Understood its limitation (risk of overfitting if test set reused) --> Intro to k-fold cross-validation.
Reset. Restart. Refocus. Wasted 10 days 😢 but back on track again. Day 8 of #ML -->Wrapped up Ch.5 with (t-SNE) t-distributed stochastic neighbour & started Ch.6 --> today I dived into : (Pipelines + Cross-validation intro). Tomorrow: Cross-validation techniques."
Reset. Restart. Refocus. Wasted 10 days 😢 but back on track again. Day 8 of #ML -->Wrapped up Ch.5 with (t-SNE) t-distributed stochastic neighbour & started Ch.6 --> today I dived into : (Pipelines + Cross-validation intro). Tomorrow: Cross-validation techniques." https://t.co/5AzZEC5qWc
Lol ! I was banned by twitter for 1 week 😭. Finally it's done
Day 7 of #ML Yesterday = Zero progress 😭 -My Engineering exams just got over and so..... Took a chill day(about Yesterday): watched Barça’s match , played eFootball and relaxed😭 Back to the book today Let’s keep the streak going Reading this quote after wasting My day😭
Day 7 of #ML Yesterday = Zero progress 😭 -My Engineering exams just got over and so..... Took a chill day(about Yesterday): watched Barça’s match , played eFootball and relaxed😭 Back to the book today Let’s keep the streak going Reading this quote after wasting My day😭 https://t.co/FtjFW3FuDp
Day 6 Of Posting #ML Journey On twitter. Topics Learnt Today : -->Non-Linear Dimensionality Reduction and Visualization -->Visualizing data via distributed stochastic neighbour embedding Almost done with the chapter now, Will wrap it up by tomorrow. Good night 😴💤
DAY 5 Did nothing today 😢 my engineering exams are starting from today , I was studying for the exam so no ML for next 2-3 days🤝🫠
Those who know the story behind this pic 💀💀
Day 4 of #ML --> Implemented LDA using sklearn Almost similar to PCA, but key difference: -->PCA → Finds axes of maximum variance. -->LDA → Finds subspace that maximizes class separability. Apart from this My Day went really bad 😞 but we still keep going.
Why does it feel like every coder here is an anime fan? Are there any tech Football fans, don't leave me hanging alone! ⚽️😂 #FootballTwitter #TechTwitter
Day 3 Of #ML ->Wrapped Up PCA 1)Decomposed Covariance Matrix to eigenpairs. 2)Transformed dataset into k-dimensional subspace (Feature Transformation) 2)Implemented PCA using Sklearn 3) Checked Feature Contributions Next: Linear Discriminant Analysis (LDA)
Kya bol rahe ho elon bhai😭 Coding chodke chai ki tapri daal du ?
DAY 2 Started The 5th chapter Of #ML book. Topics Learnt Today⬇️ 1) Intro To Unsupervised Dimensionality Reduction via Principal Component Analysis 2)PCA algorithm 3) decomposed covariance matrix into eigen pairs. 4) Extracting Principal Components 5)Total and explained Variance
Moving to Chapter No 🤚
Moving to Chapter No 🤚 https://t.co/d9tZE1We3l
DAY 1 -> Done with the 4th chapter of #ML book "Data preprocessing and Data handling" -Learn the basics of how to handle the missing values and cleaning of the data. -Worked With the "Wine Data Set" Nothing else No different or hard concepts were there in this chapter. That's it
Pehle hi 28 followers the , ab aur 2 kam ho gaye 😭😭 50 when ?
Imagine staying up until 4 am and your team bottles the 2 goal lead 😭😭 "Only those who watch football can relate 💔💔
"Interviewer: Where do you see yourself in 5 years? Him: Real." @x0_vineet 😭
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