#ScikitLearn
How to Catch Data Drift When Every Feature Looks Normal

Detect hidden shifts in feature relationships with adversarial validation and scikit-learn

Telegram AI Digest
#ai #news #scikitlearn
How to Catch Data Drift When Every Feature Looks Normal
Detect hidden shifts in feature relationships with adversarial validation and scikit-learn
towardsdatascience.com
September 28, 2026 at 8:23 PM
Your AI Assistant Wrote the Code. Who Checked the Defaults?

Five scikit-learn defaults that deserve a closer look before your next model reaches production

Telegram AI Digest
#ai #news #scikitlearn
Your AI Assistant Wrote the Code. Who Checked the Defaults?
Five scikit-learn defaults that deserve a closer look before your next model reaches production
towardsdatascience.com
September 21, 2026 at 8:25 PM
August 13, 2026 at 3:00 PM
El 80% del Machine Learning no es entrenar modelos, es preparar los datos
Un error muy común al iniciarse en la Ciencia de Datos es pensar que los algoritmos de IA devuelven magia ... www.linkedin.com/posts/jose-o...
#MachineLearning #Python #ScikitLearn #DataScience #DataCleaning
July 18, 2026 at 10:42 AM
You can train standard models locally using scikit learn on phones.

#machinelearning #scikitlearn #training
June 5, 2026 at 7:20 PM
Applied Conformal Prediction - Pro Edition Upgrade
🚀 Applied Conformal Prediction: Practical Uncertainty Quantification for Real-World ML 🚀Pro Edition Upgrade for Existing Book Owners Preorder / Early Access — chapters released progressivelyMost ML systems don’t fail because the point prediction is wrong.They fail because nobody knows when not to trust it.Applied Conformal Prediction is a practitioner-first guide to building models that produce valid, decision-ready uncertainty — prediction intervals and prediction sets you can communicate, monitor, and defend in production.This page is for current owners of the Core book who want to upgrade to the Pro Edition.📖 What the Book Covers Distribution-free uncertainty quantification with finite-sample guarantees Conformal prediction for regression and classification Practical workflows: split conformal, CV+, conditional/Mondrian methods, and online settings Diagnostics & deployment: coverage gaps, drift, non-IID data, leakage, and monitoring How to present uncertainty so decision-makers can actually use it ⭐ Pro Edition Upgrade (Recommended)The Pro Edition Upgrade is designed for readers who want to implement conformal prediction correctly — not just understand the theory.It extends the Core book with code, case studies, and advanced applied material.✅ What’s Included in the Pro Upgrade Complete Jupyter notebooks — end-to-end, production-ready Python workflows Extended case studies — applied examples in forecasting, finance, and business Advanced methods — modern extensions beyond the core conformal framework Lifetime updates — all future chapters and editions included automatically Why Upgrade?The Core Edition explains the ideas.The Pro Edition gives you the implementation playbook.With the Pro upgrade, you’ll be able to: Build trustworthy prediction intervals and sets that hold up in practice Diagnose coverage failures under shift and drift Communicate uncertainty in a way stakeholders understand and trust ⚡ Early Access Upgrade Lock in the lowest upgrade price during Early Access New content delivered progressively as chapters are released Lifetime access to all future updates included 📅 Upgrade today and turn conformal prediction from theory into a production-ready tool.
valeman.gumroad.com
June 4, 2026 at 4:33 PM
🚀 ¿Listo para dotar de un "Cerebro" a tus proyectos de código? 🧠💻
👇 Entra a la lista de reproducción completa, SUSCRÍBETE para no perderte nada y empieza a entrenar tu primer modelo:🔗 www.youtube.com/playlist?lis...
¡Nos vemos en el código! 👨‍💻
#MachineLearning #Python #ScikitLearn
May 31, 2026 at 5:50 AM
Applied Conformal Prediction - Pro Edition Upgrade
🚀 Applied Conformal Prediction: Practical Uncertainty Quantification for Real-World ML 🚀Pro Edition Upgrade for Existing Book Owners Preorder / Early Access — chapters released progressivelyMost ML systems don’t fail because the point prediction is wrong.They fail because nobody knows when not to trust it.Applied Conformal Prediction is a practitioner-first guide to building models that produce valid, decision-ready uncertainty — prediction intervals and prediction sets you can communicate, monitor, and defend in production.This page is for current owners of the Core book who want to upgrade to the Pro Edition.📖 What the Book Covers Distribution-free uncertainty quantification with finite-sample guarantees Conformal prediction for regression and classification Practical workflows: split conformal, CV+, conditional/Mondrian methods, and online settings Diagnostics & deployment: coverage gaps, drift, non-IID data, leakage, and monitoring How to present uncertainty so decision-makers can actually use it ⭐ Pro Edition Upgrade (Recommended)The Pro Edition Upgrade is designed for readers who want to implement conformal prediction correctly — not just understand the theory.It extends the Core book with code, case studies, and advanced applied material.✅ What’s Included in the Pro Upgrade Complete Jupyter notebooks — end-to-end, production-ready Python workflows Extended case studies — applied examples in forecasting, finance, and business Advanced methods — modern extensions beyond the core conformal framework Lifetime updates — all future chapters and editions included automatically Why Upgrade?The Core Edition explains the ideas.The Pro Edition gives you the implementation playbook.With the Pro upgrade, you’ll be able to: Build trustworthy prediction intervals and sets that hold up in practice Diagnose coverage failures under shift and drift Communicate uncertainty in a way stakeholders understand and trust ⚡ Early Access Upgrade Lock in the lowest upgrade price during Early Access New content delivered progressively as chapters are released Lifetime access to all future updates included 📅 Upgrade today and turn conformal prediction from theory into a production-ready tool.
valeman.gumroad.com
May 4, 2026 at 4:32 PM
¿Sabrías predecir quién sobrevivió al Titanic usando Python? 🌊🚢
De los datos brutos a la predicción final: 🔹 Limpieza con Pandas. 🔹 Entrenamiento del modelo. 🔹 Evaluación de resultados.

💻 youtu.be/C2iHKYwVNzU
#Python #MachineLearning #DataScience #ScikitLearn #AI
April 26, 2026 at 6:41 AM
Las chicas 🤝 scikitlearn
April 18, 2026 at 12:13 PM
Applied Conformal Prediction - Pro Edition Upgrade
🚀 Applied Conformal Prediction: Practical Uncertainty Quantification for Real-World ML 🚀Pro Edition Upgrade for Existing Book Owners Preorder / Early Access — chapters released progressivelyMost ML systems don’t fail because the point prediction is wrong.They fail because nobody knows when not to trust it.Applied Conformal Prediction is a practitioner-first guide to building models that produce valid, decision-ready uncertainty — prediction intervals and prediction sets you can communicate, monitor, and defend in production.This page is for current owners of the Core book who want to upgrade to the Pro Edition.📖 What the Book Covers Distribution-free uncertainty quantification with finite-sample guarantees Conformal prediction for regression and classification Practical workflows: split conformal, CV+, conditional/Mondrian methods, and online settings Diagnostics & deployment: coverage gaps, drift, non-IID data, leakage, and monitoring How to present uncertainty so decision-makers can actually use it ⭐ Pro Edition Upgrade (Recommended)The Pro Edition Upgrade is designed for readers who want to implement conformal prediction correctly — not just understand the theory.It extends the Core book with code, case studies, and advanced applied material.✅ What’s Included in the Pro Upgrade Complete Jupyter notebooks — end-to-end, production-ready Python workflows Extended case studies — applied examples in forecasting, finance, and business Advanced methods — modern extensions beyond the core conformal framework Lifetime updates — all future chapters and editions included automatically Why Upgrade?The Core Edition explains the ideas.The Pro Edition gives you the implementation playbook.With the Pro upgrade, you’ll be able to: Build trustworthy prediction intervals and sets that hold up in practice Diagnose coverage failures under shift and drift Communicate uncertainty in a way stakeholders understand and trust ⚡ Early Access Upgrade Lock in the lowest upgrade price during Early Access New content delivered progressively as chapters are released Lifetime access to all future updates included 📅 Upgrade today and turn conformal prediction from theory into a production-ready tool.
valeman.gumroad.com
April 4, 2026 at 4:31 PM
📘 He lanzado este libro para que tengas toda la teoría, los diagramas y los ejercicios organizados en un solo lugar. ✅ El Pack Completo www.amazon.es/dp/B0CW17NBGM
La lista de reproducción con las clases prácticas. www.youtube.com/playlist?lis... #MachineLearning #Python #ScikitLearn
April 4, 2026 at 11:18 AM
🌳 ¿Cómo toma decisiones una IA? Desbloqueamos los Árboles de Decisión con #Python y #ScikitLearn. 💻✨
💎 ¡ACCESO ANTICIPADO! El vídeo ya está disponible en exclusiva para los suscriptores del canal. 🛡️
¡No esperes más y domina el #MachineLearning! 🚀🐍
www.youtube.com/watch?v=-uxv...
March 15, 2026 at 8:31 AM
Scikit-learn just took a leap toward GPU-accelerated machine learning, and the Python community made it happen. 🚀

The latest Quansight blog by Lucy Liu gives the full update: buff.ly/RCcPty1

#ScikitLearn #MachineLearning #OpenSource #Python #GPU #ArrayAPI #DataScience #MLOps
March 6, 2026 at 1:00 PM
Applied Conformal Prediction - Pro Edition Upgrade
🚀 Applied Conformal Prediction: Practical Uncertainty Quantification for Real-World ML 🚀Pro Edition Upgrade for Existing Book Owners Preorder / Early Access — chapters released progressivelyMost ML systems don’t fail because the point prediction is wrong.They fail because nobody knows when not to trust it.Applied Conformal Prediction is a practitioner-first guide to building models that produce valid, decision-ready uncertainty — prediction intervals and prediction sets you can communicate, monitor, and defend in production.This page is for current owners of the Core book who want to upgrade to the Pro Edition.📖 What the Book Covers Distribution-free uncertainty quantification with finite-sample guarantees Conformal prediction for regression and classification Practical workflows: split conformal, CV+, conditional/Mondrian methods, and online settings Diagnostics & deployment: coverage gaps, drift, non-IID data, leakage, and monitoring How to present uncertainty so decision-makers can actually use it ⭐ Pro Edition Upgrade (Recommended)The Pro Edition Upgrade is designed for readers who want to implement conformal prediction correctly — not just understand the theory.It extends the Core book with code, case studies, and advanced applied material.✅ What’s Included in the Pro Upgrade Complete Jupyter notebooks — end-to-end, production-ready Python workflows Extended case studies — applied examples in forecasting, finance, and business Advanced methods — modern extensions beyond the core conformal framework Lifetime updates — all future chapters and editions included automatically Why Upgrade?The Core Edition explains the ideas.The Pro Edition gives you the implementation playbook.With the Pro upgrade, you’ll be able to: Build trustworthy prediction intervals and sets that hold up in practice Diagnose coverage failures under shift and drift Communicate uncertainty in a way stakeholders understand and trust ⚡ Early Access Upgrade Lock in the lowest upgrade price during Early Access New content delivered progressively as chapters are released Lifetime access to all future updates included 📅 Upgrade today and turn conformal prediction from theory into a production-ready tool.
valeman.gumroad.com
March 4, 2026 at 4:30 PM
🔮 ¿Y si pudieras "adivinar" el futuro usando datos y matemáticas? 📈🤖
En Machine Learning no necesitamos una bola de cristal, ¡usamos la Regresión Lineal!
👉 youtu.be/Vybj0CUvojs
#MachineLearning #Python #ScikitLearn #RegresionLineal #DataScience #InteligenciaArtificial
February 28, 2026 at 9:05 AM
🚀 ¡Llegó el gran día! Hemos encendido nuestra primera Inteligencia Artificial 🤖🧠
www.linkedin.com/feed/update/...

📺 No esperes a mayo, suscríbete y mira el vídeo aquí:
👉https://www.youtube.com/watch?v=RCpVPKw9-9c

#MachineLearning #Python #ScikitLearn #DataScience #InteligenciaArtificial
February 22, 2026 at 7:57 PM
🚨 ¡NUEVO VÍDEO DISPONIBLE! (Acceso anticipado) 🚨
¿Alguna vez has intentado comparar peras con manzanas? 🍎🍏 En Machine Learning, hacer esto puede arruinar tu inteligencia artificial.
👉 www.youtube.com/watch?v=6cyq...
#MachineLearning #DataScience #Python #ScikitLearn #StandardScaler
February 21, 2026 at 7:36 AM
Next up: beginner-friendly PRs, revising Python/Pandas/NumPy, mini ML projects, and documenting them publicly.

Building visibility and credibility one step at a time!

Follow up, Let's build. #AI #ML #Python #OpenSource #ScikitLearn #CareerGrowth
February 17, 2026 at 10:02 AM
Things I worked through today:

- Column clustering into graph object representing correlation between variables
- Apple's Developer App - don't even get me started

#genAI #llm #statistics #dataTypes #dataFormats #analystProgrammer #data #python #statsmodels #scikitLearn #rustLang #swift #iOS
February 4, 2026 at 3:21 AM
Want to supercharge your ML models? Learn how to embed preprocessing pipelines directly into hyperparameter tuning with Scikit-learn tricks. Boost performance and keep your code clean. Dive in! #ScikitLearn #Preprocessing #HyperparameterTuning

🔗 aidailypost.com/news/7-sciki...
January 29, 2026 at 5:54 PM
I do think it's not quite been a $1M/yr job for a while: by the early 2020s there was a ton of growth in Data Science MS programs that could quickly (~18 mos) upskill students with some stats+scikitlearn+pytorch to get $100-150k jobs doing that kind of applied ML. But that's certainly a big segment!
January 28, 2026 at 7:56 PM
I'm finding it's somewhat easier to create AI business apps for structured data (SQL, python, pandas, scikitLearn, etc) vs AI with rag for unstructured documents... but in both cases , wrap the AI with lots of code logic, rules and guardrails.. thirsty work!

www.youtube.com/watch?v=iqD0...
2 year project for AI and Business Software v129
YouTube video by Steve King (steve123go)
www.youtube.com
January 1, 2026 at 11:22 PM
Just built a quick ML API with FastAPI, uvicorn, scikit‑learn and pydantic—served the classic Iris dataset and auto‑generated Swagger docs. Want the step‑by‑step install guide? Dive in! #FastAPI #scikitlearn #pydantic

🔗 aidailypost.com/news/fastapi...
December 10, 2025 at 4:35 PM