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WTwajahat tajai-ml-datascience-from-scratch.hashnode.devJun 16 · 6 min read

Building a Municipal AI Complaint System FastAPI, ML, PostgreSQL, and Docker

I have been working on a project that I think is actually useful in a real-world context, a municipal complaint management system that uses machine learning to automatically categorize, score, and pri

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NNNaguru Navya Nikhithaneuralnotes-ai.hashnode.devJun 1 · 4 min read

10 Scikit-learn Functions Every ML Beginner Should Know

You've cleaned your data. You've visualized it. Now it's time to build models. Scikit-learn is the most used machine learning library in Python. Here are the 10 functions you'll use in every single ML

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MMMungara Muddu Krishna Yadavmuddukrishna.hashnode.devMay 19 · 13 min read

Building Bulletproof ML Pipelines: Feature Engineering, Scaling & Leakage-Free Preprocessing

Part 2 of the ML Engineering series — where clean data meets production-ready code. Why pipelines matter more than your algorithm choice There's a humbling pattern every ML engineer eventually faces:

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RARoberto Andrade Fonsecarandradedev.hashnode.devApr 29 · 5 min read

Heil Mary. Repensando la enseñanza de la física desde la ciencia ficción

He pasado años enseñando ciencia y tecnología. Además soy un lector frecuente de la ciencia ficción, a veces con la misma intensidad con la que preparo una clase o aprendo sobre TIC o AI . En algún pu

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HHHenry Hadatabeginnersfriendlytutorials.hashnode.devApr 15 · 26 min read

Cross-Validation, Pipelines & Hyperparameter Tuning with Scikit-learn

When building a machine learning model, it's tempting to train it on your data, check the score, and call it done. But that score is misleading — the model has already "seen" the data it's being evalu

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AAAbstract Algorithmsabstractalgorithms.devMar 29 · 19 min read

Feature Engineering: Transforming Raw Data into ML-Ready Features

TLDR: 🛠️ Feature engineering transforms messy real-world data into ML-compatible input. Bad features break even the best models — good features make simple algorithms shine. This guide covers scaling

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DSDumpali Sharath chandra Reddyhar-sharathcd.hashnode.devMar 16 · 6 min read

How I Benchmarked 15 Machine Learning Algorithms for Human Activity Recognition — and Hit 0.9682 Precision

By Sharath Chandra Reddy Dumpali · M.S. Computer Science, University of New Haven The Problem Your smartphone knows when you're walking, running, or sitting — but how does it actually figure that out

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TDTim Derzhavetstimderzhavets.hashnode.devFeb 24 · 14 min read

Exercise Selection Algorithms with Equipment Constraints

Introduction Building a fitness application that recommends exercises sounds straightforward until you encounter the complexity of real-world constraints. A user wants to build upper body strength, but they're working out at home with only dumbbells ...

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AIAdil Ijazadilijazdev.hashnode.devFeb 22 · 3 min read

🫀 Heart Disease Prediction Using Machine Learning

Heart disease remains one of the leading causes of death worldwide. In this project, I built a binary classification model that predicts whether a patient has heart disease or not using structured med

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TDTim Derzhavetstimderzhavets.hashnode.devFeb 20 · 14 min read

Exercise Selection Algorithms with Equipment Constraints

Introduction Building a fitness application that recommends exercises sounds straightforward until you encounter the complexity of real-world constraints. A user wants to build upper body strength, but they're working out at home with only dumbbells ...

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#scikit-learn

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#scikit-learn

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Trending tags this week

Building a Municipal AI Complaint System FastAPI, ML, PostgreSQL, and Docker

10 Scikit-learn Functions Every ML Beginner Should Know

Building Bulletproof ML Pipelines: Feature Engineering, Scaling & Leakage-Free Preprocessing

Heil Mary. Repensando la enseñanza de la física desde la ciencia ficción

Cross-Validation, Pipelines & Hyperparameter Tuning with Scikit-learn

Feature Engineering: Transforming Raw Data into ML-Ready Features

How I Benchmarked 15 Machine Learning Algorithms for Human Activity Recognition — and Hit 0.9682 Precision

Exercise Selection Algorithms with Equipment Constraints

🫀 Heart Disease Prediction Using Machine Learning

Exercise Selection Algorithms with Equipment Constraints