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Data Science

Data Science allows us to extract meaning from and interpret data.

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AWS Data & AI Stories #01: Multimodal AI
Cover image for AWS Data & AI Stories #01: Multimodal AI

AWS Data & AI Stories #01: Multimodal AI

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4 min read
Your Dashboard Numbers Are Wrong — And Your Team Is Making Decisions Based on Them

Your Dashboard Numbers Are Wrong — And Your Team Is Making Decisions Based on Them

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3 min read
My First Time With SQL

My First Time With SQL

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3 min read
Another week, another small step forward

Another week, another small step forward

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1 min read
CNN Training Isn’t Just About Models — Augmentation vs Preprocessing vs BatchNorm

CNN Training Isn’t Just About Models — Augmentation vs Preprocessing vs BatchNorm

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4 min read
Linear Models in Machine Learning: Why They Still Matter (Regression, Classification, Logistic Regression)

Linear Models in Machine Learning: Why They Still Matter (Regression, Classification, Logistic Regression)

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2 min read
What Machine Learning Really Means: From Rules to Data-Driven Systems

What Machine Learning Really Means: From Rules to Data-Driven Systems

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6 min read
Crucix: An Open OSINT Dashboard Aggregating 27 Real-Time Data Sources

Crucix: An Open OSINT Dashboard Aggregating 27 Real-Time Data Sources

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1 min read
The Best Python Library for Generating Quick Synthetic Data in 2026

The Best Python Library for Generating Quick Synthetic Data in 2026

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11 min read
#Service Page Isn’t Ranking Fix Using Schema

#Service Page Isn’t Ranking Fix Using Schema

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2 min read
Hackathon Project: How I built an AI Recommender System for SMEs
Cover image for Hackathon Project: How I built an AI Recommender System for SMEs

Hackathon Project: How I built an AI Recommender System for SMEs

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2 min read
Stop writing pandas.merge() by hand: I built a Visual Join Builder for VS Code đź§©

Stop writing pandas.merge() by hand: I built a Visual Join Builder for VS Code đź§©

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1 min read
Machine Learning Tasks and Evaluation: How to Choose the Right Metrics and Avoid Common Pitfalls

Machine Learning Tasks and Evaluation: How to Choose the Right Metrics and Avoid Common Pitfalls

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2 min read
Model Complexity and Generalization: How to Actually Fix Overfitting

Model Complexity and Generalization: How to Actually Fix Overfitting

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2 min read
Traditional Machine Learning in Practice: Learning Paradigms, Algorithm Families, and Evaluation Perspectives

Traditional Machine Learning in Practice: Learning Paradigms, Algorithm Families, and Evaluation Perspectives

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2 min read
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