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ROC–AUC comes from signal detection theory from the 1940s radar systems. Scientists were literally trying to separate enemy aircraft signals from noise. Now we use the same math to detect fraud, disea
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Accuracy isn’t enough for classification models. Precision, Recall, and F1 Score reveal how well your model handles false positives and false negatives. #machinelearning #artificialintelligence #codi
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If the interviewer asks about Logistic Regression, mention that it uses the sigmoid function to convert linear output into probability (0–1). Quick Recall: Logistic Regression = Linear model + Sigmoi
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Logistic regression trains on log loss But we judge it using Accuracy Precision Recall F1 score ROC AUC We use log loss for training because we need a smooth surface so gradient descent can comp
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A classifier can improve accuracy by becoming less confident? In modern deep learning, techniques like label smoothing deliberately prevent the model from assigning probability 1.0 to any class during
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In production ML systems, the model often degrades even if the code never changes. Why? Because the data changes. This is called data drift and concept drift, where: User behavior shifts Business
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Univariate, multiple, and multivariate regression are the same model scaled up — from 1 predictor → many predictors → many outcomes, all minimizing error with the same math. #machinelearning #artific
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Degree 2 and 3 are the most practical. Quadratic and cubic models often capture curvature well without becoming unstable #machinelearning #artificialintelligence #coding #computerscience #program
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A model can have high bias and high variance at the same time People think it’s one or the other. In reality, bad feature design or data leakage can cause both simultaneously #machinelearning #artif
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A model with 99% training accuracy can still perform worse than a much simpler model on real-world data—because it’s overfitting and memorizing noise instead of learning patterns. #machinelearning #
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In regression tasks, a model can have a low average error but still make huge mistakes on rare cases—because common metrics like MSE and MAE heavily reward being “mostly right,” not consistently right
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Did you know linear regression will confidently draw a straight line through data it clearly doesn’t understand? You can give it a perfect curve, a circle, or a banana-shaped pattern, and linear regr
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About @nerdyycore

aaishni is a Instagram growing creator in the Education category. With 55 followers and 12 posts, nerdyycore's account shows a follower-to-following ratio of 2.0:1. View all content anonymously on Pictame - no login required.

nerdyycore's engagement rate sits at 12.73%. That's 155% above what you'd typically see for accounts with 1K-10K followers (average 5%) — a sign of a genuinely connected audience.

Out of the last 12 posts, 100% are videos and 0% are photos. Content shows up about every 4 days. Captions lean moderate, averaging 328 characters.

12.73% engagement rate — 155% above average for 1K-10K follower accounts.
Posts about every 4 days.
2.0:1 follower-to-following ratio.

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nerdyycore
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@nerdyycore

aaishni

PublicEducation
Posts
12
Followers
55
Top Hashtags
#artificialintelligence#machinelearning#coding#programming#computerscience#study#ml#aiml

Explore the most impactful hashtags used in recent posts. These tags highlight the core interests and trending topics that define this profile's digital influence and content strategy.

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Popular Hashtags

#influencer#photographer#naturephotography#landscape#travelphotography#architecture#portrait#wanderlust#adventure#vacation#trip
Following
27
Avg. Likes
-1
Avg. Comments
8
Engagement
12.73%

Backend Engineer👩🏻‍🏫 plotting on the inside (📊) ML • Engineering • stuff Learn ML with me from the basics to the cor...

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