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Sachin Kr. Rajput

Building production-grade AI systems: RAG pipelines, multi-agent architectures, and LLM-powered tools. Currently obsessed with: Agentic AI, VectorDBs, , and making AI actually work in production.

Location India Joined Joined on 

Work

Senior Engineering Manager at Guidepoint - QSight

Bagging: The Jury System That Taught Machine Learning the Wisdom of Crowds
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Bagging: The Jury System That Taught Machine Learning the Wisdom of Crowds

#machinelearning #datascience #python #beginners
Comments 2
16 min read

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Pruning Decision Trees: The Bonsai Master Who Taught ML Engineers When to Stop
sachin_krrajput profile

Pruning Decision Trees: The Bonsai Master Who Taught ML Engineers When to Stop

#machinelearning #datascience #python #beginners
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16 min read
Gini Impurity: The Blindfolded Archer Who Taught Decision Trees How to Split
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Gini Impurity: The Blindfolded Archer Who Taught Decision Trees How to Split

#machinelearning #datascience #python #beginners
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15 min read
Information Gain & Entropy: The Game Show Host Who Learned to Ask Perfect Questions
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Information Gain & Entropy: The Game Show Host Who Learned to Ask Perfect Questions

#machinelearning #datascience #python #beginners
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16 min read
Decision Trees: The Detective Who Solves Cases by Asking Yes/No Questions
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Decision Trees: The Detective Who Solves Cases by Asking Yes/No Questions

#machinelearning #datascience #python #beginners
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16 min read
The Sigmoid Function: The Story of the World's Most Diplomatic Mathematician
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The Sigmoid Function: The Story of the World's Most Diplomatic Mathematician

#machinelearning #datascience #python #beginners
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10 min read
Why Is It Called 'Logistic Regression' If It's Used for Classification? The Naming Mystery Explained
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Why Is It Called 'Logistic Regression' If It's Used for Classification? The Naming Mystery Explained

#machinelearning #datascience #python #beginners
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8 min read
Logistic Regression: The Bouncer Who Gives Probability of Entry Instead of Just Yes/No
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Logistic Regression: The Bouncer Who Gives Probability of Entry Instead of Just Yes/No

#machinelearning #datascience #python #beginners
Comments
13 min read
Elastic Net: The Mediator Who Said 'Let's Take the Best of Both Approaches'
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Elastic Net: The Mediator Who Said 'Let's Take the Best of Both Approaches'

#machinelearning #datascience #python #beginners
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11 min read
Lasso Regression: The Brutal Manager Who Said 'Some of You Are Getting Fired' — And Actually Did It
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Lasso Regression: The Brutal Manager Who Said 'Some of You Are Getting Fired' — And Actually Did It

#machinelearning #datascience #python #beginners
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12 min read
Ridge Regression: The Manager Who Said 'Everyone Gets a Small Piece' Instead of 'Winner Takes All'
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Ridge Regression: The Manager Who Said 'Everyone Gets a Small Piece' Instead of 'Winner Takes All'

#machinelearning #datascience #python #beginners
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12 min read
Multicollinearity: The Three Witnesses Who Told the Same Story — And Why the Jury Got Confused
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Multicollinearity: The Three Witnesses Who Told the Same Story — And Why the Jury Got Confused

#machinelearning #datascience #python #beginners
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12 min read
When Linear Regression Assumptions Are Violated: The Bridge Engineer Who Ignored the Cracks and Declared It Safe
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When Linear Regression Assumptions Are Violated: The Bridge Engineer Who Ignored the Cracks and Declared It Safe

#machinelearning #datascience #python #beginners
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12 min read
The Assumptions of Linear Regression: The GPS That Only Works on Straight, Flat Roads in Perfect Weather
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The Assumptions of Linear Regression: The GPS That Only Works on Straight, Flat Roads in Perfect Weather

#machinelearning #datascience #python #beginners
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13 min read
How Linear Regression Works: The Lazy Architect Who Drew One Line and Called It a Floor Plan
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How Linear Regression Works: The Lazy Architect Who Drew One Line and Called It a Floor Plan

#machinelearning #datascience #python #beginners
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11 min read
Is Your Model Good Enough to Deploy? The Restaurant That Served a 95% Perfect Dish — But the 5% Was Food Poisoning
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Is Your Model Good Enough to Deploy? The Restaurant That Served a 95% Perfect Dish — But the 5% Was Food Poisoning

#machinelearning #datascience #python #beginners
Comments
13 min read
Validation Set vs Test Set: The Student Who Aced Every Practice SAT But Bombed the Real One
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Validation Set vs Test Set: The Student Who Aced Every Practice SAT But Bombed the Real One

#machinelearning #datascience #python #beginners
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10 min read
K-Fold Cross-Validation: The Comedian Who Tested Jokes at Only One Comedy Club and Bombed Everywhere Else
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K-Fold Cross-Validation: The Comedian Who Tested Jokes at Only One Comedy Club and Bombed Everywhere Else

#machinelearning #datascience #python #beginners
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13 min read
Stratified Sampling: The Pollster Who Predicted a Landslide by Accidentally Surveying Only One Neighborhood
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Stratified Sampling: The Pollster Who Predicted a Landslide by Accidentally Surveying Only One Neighborhood

#machinelearning #datascience #python #beginners
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11 min read
Choosing the Right Metric: The Restaurant Inspector Who Judged Every Kitchen by Decor
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Choosing the Right Metric: The Restaurant Inspector Who Judged Every Kitchen by Decor

#machinelearning #datascience #python #beginners
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9 min read
Type I vs Type II Errors: The Fire Alarm That Cried Wolf vs The Fire Alarm That Slept Through Arson
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Type I vs Type II Errors: The Fire Alarm That Cried Wolf vs The Fire Alarm That Slept Through Arson

#machinelearning #datascience #python #beginners
Comments
11 min read
MAE vs MSE vs RMSE: Three Bosses With Very Different Philosophies on Punishing Late Employees
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MAE vs MSE vs RMSE: Three Bosses With Very Different Philosophies on Punishing Late Employees

#machinelearning #datascience #python #beginners
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10 min read
R-Squared Explained: The Dart Player Who's Somehow WORSE Than Just Aiming at the Center
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R-Squared Explained: The Dart Player Who's Somehow WORSE Than Just Aiming at the Center

#machinelearning #datascience #python #beginners
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11 min read
Log Loss Explained: The Game Show Where Confidence Costs You — Being Wrong Is Bad, Being CONFIDENTLY Wrong Is Catastrophic
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Log Loss Explained: The Game Show Where Confidence Costs You — Being Wrong Is Bad, Being CONFIDENTLY Wrong Is Catastrophic

#machinelearning #datascience #python #beginners
Comments
12 min read
AUC-ROC Explained: The Smoke Detector With a Sensitivity Dial and the One Number That Tells You If It's Any Good
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AUC-ROC Explained: The Smoke Detector With a Sensitivity Dial and the One Number That Tells You If It's Any Good

#machinelearning #datascience #python #beginners
Comments
11 min read
The Confusion Matrix: A Courtroom Drama Where Every Verdict Falls Into One of Four Boxes
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The Confusion Matrix: A Courtroom Drama Where Every Verdict Falls Into One of Four Boxes

#machinelearning #datascience #python #beginners
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11 min read
When Accuracy Is a Lying Metric: The Weather Forecaster Who Was 96% Accurate and Still Got Everyone Killed
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When Accuracy Is a Lying Metric: The Weather Forecaster Who Was 96% Accurate and Still Got Everyone Killed

#machinelearning #datascience #python #beginners
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10 min read
Accuracy, Precision, Recall, F1: The Four Judges Who Disagree on What Makes a Good Wolf Detector
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Accuracy, Precision, Recall, F1: The Four Judges Who Disagree on What Makes a Good Wolf Detector

#machinelearning #datascience #python #beginners
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10 min read
Train/Validation/Test Split: Why Your Model Needs Practice, Dress Rehearsals, AND Opening Night
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Train/Validation/Test Split: Why Your Model Needs Practice, Dress Rehearsals, AND Opening Night

#machinelearning #datascience #python #beginners
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10 min read
PCA Explained: Finding the Perfect Angle to Photograph a Sculpture So You Capture Everything in One Shot
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PCA Explained: Finding the Perfect Angle to Photograph a Sculpture So You Capture Everything in One Shot

#datascience #python #beginners #programming
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10 min read
Feature Selection vs Feature Extraction: Choosing Your Best Songs vs Remixing Them Into Something New
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Feature Selection vs Feature Extraction: Choosing Your Best Songs vs Remixing Them Into Something New

#datascience #python #beginners #programming
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11 min read
Feature Engineering: The Dark Art of Teaching Your Model to See What You See
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Feature Engineering: The Dark Art of Teaching Your Model to See What You See

#datascience #python #beginners #programming
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12 min read
Outliers: The Art of Deciding Whether That 3,000 kg Penguin Is a Data Entry Error or an Actual Monster
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Outliers: The Art of Deciding Whether That 3,000 kg Penguin Is a Data Entry Error or an Actual Monster

#datascience #python #beginners #programming
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13 min read
Data Leakage: The Silent Killer That Makes Your Model a Cheating Genius in Training and a Complete Idiot in Production
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Data Leakage: The Silent Killer That Makes Your Model a Cheating Genius in Training and a Complete Idiot in Production

#datascience #python #beginners #programming
Comments 1
10 min read
SMOTE: The Creature Creation Lab That Saves Your Minority Class From Extinction
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SMOTE: The Creature Creation Lab That Saves Your Minority Class From Extinction

#datascience #python #beginners #programming
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10 min read
Imbalanced Datasets: When Your Model Gets 99% Accuracy by Being Completely Useless
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Imbalanced Datasets: When Your Model Gets 99% Accuracy by Being Completely Useless

#datascience #python #beginners #programming
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11 min read
Label Encoding: The Simple Trick That's Either Genius or Disaster Depending on One Question
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Label Encoding: The Simple Trick That's Either Genius or Disaster Depending on One Question

#datascience #python #beginners #programming
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9 min read
One-Hot Encoding: The Genius Trick That Works Perfectly Until It Explodes Your Computer
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One-Hot Encoding: The Genius Trick That Works Perfectly Until It Explodes Your Computer

#datascience #python #beginners #programming
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11 min read
Handling Categorical Variables: Teaching Your Model to Understand "Red," "Blue," and "Green" Without Having a Mental Breakdown
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Handling Categorical Variables: Teaching Your Model to Understand "Red," "Blue," and "Green" Without Having a Mental Breakdown

#datascience #python #beginners #programming
Comments
11 min read
Normalization vs Standardization: The Tale of Two Translators Who Speak Different Languages
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Normalization vs Standardization: The Tale of Two Translators Who Speak Different Languages

#datascience #python #beginners #programming
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11 min read
Feature Scaling: Why Your Model Thinks a $50,000 Salary Matters More Than 20 Years of Experience
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Feature Scaling: Why Your Model Thinks a $50,000 Salary Matters More Than 20 Years of Experience

#datascience #python #beginners #programming
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11 min read
Handling Missing Data: The Detective's Guide to Solving the Case of the Vanishing Values
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Handling Missing Data: The Detective's Guide to Solving the Case of the Vanishing Values

#datascience #python #beginners #programming
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12 min read
Generative vs Discriminative Models: The Artist Who Paints vs The Critic Who Points
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Generative vs Discriminative Models: The Artist Who Paints vs The Critic Who Points

#machinelearning #ai #beginners #datascience
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9 min read
The Curse of Dimensionality: Why More Features Can Destroy Your Model Instead of Saving It
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The Curse of Dimensionality: Why More Features Can Destroy Your Model Instead of Saving It

#machinelearning #ai #beginners #datascience
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9 min read
Cross-Validation: Why Testing Your Model Once Is Like Judging a Restaurant by a Single Bite
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Cross-Validation: Why Testing Your Model Once Is Like Judging a Restaurant by a Single Bite

#machinelearning #ai #beginners #datascience
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11 min read
L1 vs L2 Regularization: The Minimalist vs The Diplomat — Two Philosophies That Shape Your Model
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L1 vs L2 Regularization: The Minimalist vs The Diplomat — Two Philosophies That Shape Your Model

#machinelearning #ai #beginners #datascience
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10 min read
Regularization: The Art of Telling Your Model to Calm Down and Stop Overthinking
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Regularization: The Art of Telling Your Model to Calm Down and Stop Overthinking

#machinelearning #ai #beginners #datascience
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10 min read
Learning Rate: The One Number That Can Make or Break Your Entire Model
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Learning Rate: The One Number That Can Make or Break Your Entire Model

#machinelearning #ai #beginners #datascience
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10 min read
Batch vs Mini-Batch vs Stochastic Gradient Descent: Three Hikers, Three Strategies, One Mountain
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Batch vs Mini-Batch vs Stochastic Gradient Descent: Three Hikers, Three Strategies, One Mountain

#machinelearning #ai #beginners #datascience
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10 min read
Gradient Descent: How to Find the Lowest Point in a Valley While Completely Blindfolded
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Gradient Descent: How to Find the Lowest Point in a Valley While Completely Blindfolded

#machinelearning #ai #beginners #datascience
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10 min read
Loss Functions: The Brutally Honest Friend Your Model Desperately Needs
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Loss Functions: The Brutally Honest Friend Your Model Desperately Needs

#machinelearning #ai #beginners #datascience
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9 min read
Classification vs Regression: The Doctor Who Gives Answers vs The Doctor Who Gives Numbers
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Classification vs Regression: The Doctor Who Gives Answers vs The Doctor Who Gives Numbers

#machinelearning #ai #beginners #datascience
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9 min read
Parametric vs Non-Parametric Models: The GPS vs The Taxi Driver
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Parametric vs Non-Parametric Models: The GPS vs The Taxi Driver

#machinelearning #ai #beginners #datascience
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9 min read
Overfitting & Underfitting: Why Your Model Aced the Practice Test But Failed the Real Exam
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Overfitting & Underfitting: Why Your Model Aced the Practice Test But Failed the Real Exam

#machinelearning #ai #beginners #datascience
Comments
10 min read
The Bias-Variance Tradeoff: Why Your Model is Either Too Dumb or Too Smart
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The Bias-Variance Tradeoff: Why Your Model is Either Too Dumb or Too Smart

#machinelearning #ai #beginners #datascience
Comments
7 min read
The Three Musketeers of Machine Learning: A Journey from "What's ML?" to "I Get It!"
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The Three Musketeers of Machine Learning: A Journey from "What's ML?" to "I Get It!"

#machinelearning #ai #beginners #tutorial
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9 min read
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