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ai series launching soon 🚀 follow for updates @datasciencewithdhrumil #viralreeĺs #public #ai
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building a machine learning model is only half the job. the real performance comes from how well you tune it. hyperparameter tuning helps find the best configuration for a model — improving accuracy
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machine learning models don’t understand units they understand numbers. when features have different scales, models can give more importance to larger values, leading to poor performance. feature sc
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a model that is too complex will not always perform better. in machine learning, regularization helps prevent overfitting by controlling how much a model learns from the data. l1 regularization simp
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not all machine learning errors are the same. bias occurs when a model is too simple and fails to capture patterns in the data. variance occurs when a model is too complex and learns noise instead of
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a good machine learning model should not be too simple and not too complex. underfitting happens when a model fails to learn the patterns in the data. overfitting happens when a model memorizes the t
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accuracy from a single train-test split can sometimes be misleading. that’s why data scientists use k-fold cross-validation to evaluate machine learning models more reliably. instead of testing the
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accuracy alone doesn’t tell the full story of a machine learning model. when dealing with real-world classification problems, we need a metric that balances precision and recall. that’s where the f1
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accuracy alone doesn’t tell the full story of a machine learning model. precision and recall help us understand how reliable a model is when making positive predictions and how many real positive cas
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Understanding how good your machine learning model really is goes beyond just accuracy. The ROC Curve (Receiver Operating Characteristic) helps data scientists evaluate how well a classification mode
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Follow @datasciencewithdhrumil for practical data science content. #datascience #machinelearning #python #ai #datasciencetips
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machine learning is not just one algorithm it’s an entire ecosystem. from regression and classification to anomaly detection and reinforcement learning, each technique solves a different kind of prob
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About @datasciencewithdhrumil

Data Science with Dhrumil is a Instagram growing creator. With 25 followers and 34 posts, datasciencewithdhrumil's account shows a follower-to-following ratio of 1.1:1. View all content anonymously on Pictame - no login required.

datasciencewithdhrumil's engagement rate sits at 26.67%. That's 433% 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, 83% are videos and 17% are photos. New posts appear roughly every 2 days — a steady, reliable rhythm. Captions lean detailed, averaging 563 characters.

26.67% engagement rate — 433% above average for 1K-10K follower accounts.
New content every 2 days — a steady rhythm.
1.1:1 follower-to-following ratio.

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datasciencewithdhrumil
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@datasciencewithdhrumil

Data Science with Dhrumil

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#machinelearning#datascience#artificialintelligence#python#mlalgorithms#ai#viral#virelreels

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Data Science | AI | ML Building real projects Multi-Agent Systems & GenAI Learn by building

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