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.

🚀 Pro Edition: valeman.gumroad.com/...
#MachineLearning #Calibration #ConformalPrediction #DataScience #ScikitLearn #AI #Statistics

📘 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

🌳 ¿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...

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

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.

🚀 Pro Edition: valeman.gumroad.com/...
#MachineLearning #Calibration #ConformalPrediction #DataScience #ScikitLearn #AI #Statistics

🔮 ¿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

🚀 ¡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

🚨 ¡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

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

Rendre un modèle de Machine Learning compréhensible pour un non-tech ? Oui, c’est possible. | Sofiane Chehboune De la boîte noire à la transparence : appliquer l’XAI à la prédiction boursière

www.linkedin.com/posts/sofian...

#python #shapash #plotly #scikitlearn #pandas #Kaggle

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

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...

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...

Python Open-Source Developer Posit is hiring a Python open-source developer to create more data analysis tools.

We're hiring an open-source #python developer focused on modeling APIs!

tidyverse.org/blog/2025/11...

#numpy #scipy #scikitlearn

Ever wondered if you should drop rows with NaNs before training? Our latest open‑source guide shows how Pandas & scikit‑learn handle missing values, target vs predictors, and why sometimes dropping beats imputation. Dive in! #Pandas #ScikitLearn #MissingValues

🔗 aidailypost.com/news/first-p...

Some essential libraries to explore:

🧠 #LangChain – building with LLMs
🔗 #MCP – connecting tools and models
💬 #Transformers – for #NLP
⚙️ #PyTorch, #TensorFlow, #ScikitLearn – for heavy lifting
👇

The Ultimate 13-Step Scikit-Learn Crash Course for Flawless ML Models Are you ready to dive into the world of machine learning but feel overwhelmed by the complexity? You're in the right place. This Scikit-Learn Crash Course is your definitive, step-by-step guide to mastering one of Python's most powerful and user-friendly machine learning libraries. This isn't a dense, theoretical lecture on machine learning algorithms. Instead, this is a hands-on, practical tutorial designed to teach you how to use Scikit-Learn as a tool.

nlock ML mastery with this Scikit-Learn tutorial! Step-by-step guidance to build & optimize models. Perfect for beginners! #MachineLearning #Python #ScikitLearn #Tutorial

Users deserve better.

#calibration #scikitlearn

In fact, there is virtually no need for scikit-learn at all—unless one insists on using outdated methods like Naive Bayes.

#scikitlearn #datascience

A graphic illustrating data science and big data technologies. On the left, stacked vertically, are the logos for R and Python. In the center, also stacked vertically, are the Orbital logo (featuring a satellite) and the Scala logo (a blue serpent). On the right, stacked vertically, are the Databricks logo and a generic database cylinder icon. The background is a light blue with a subtle, dark blue dot pattern at the bottom.

Announcing streamlined MLOps with Orbital on Databricks 🛰️🧱

Orbital translates #ScikitLearn #Python or #tidymodels #RStats to native #SQL for direct database model execution.

@theotheredgar.bsky.social's post uses Databricks as an integrated environment.

Learn more: posit.co/blog/databri...

The Power of Building from Scratch Mauro Di Pietro discusses building AI agents with open-source tools, bridging theory and practice, and why he’s still nostalgic for scikit-learn.

The Power of Building from Scratch

Mauro Di Pietro discusses building AI agents with open-source tools, bridging theory and practice, and why he’s still nostalgic for scikit-learn.

#ai #news #scikitlearn

Workshop Wednesday: Engineering Star Trek with Data Science YouTube video by The Bajoran Engineer @ SciPy 2025

Successful 1sr live stream of the series! We scoped a few projects for an hour. Next week we start building. #scikitlearn #scikitimage #tensorflow #rapids #cuML #pyscript #numba #scipy #jupyter #wagtail

Check the replay: www.youtube.com/live/aHunV_U...

An Introduction to Python for Data Science Python is a powerful and versatile programming language widely used in data science. Its simple syntax, extensive libraries, and strong community support make it a preferred ...

An Introduction to Python for Data Science #Python #Datascience #Numpy #Pandas #Matplotlib #Scikitlearn #Library #Analysis #Visualization #Modeling #Regression #Machinelearning #Array #Meaning

2/
Its ecosystem grew fast: #NumPy, #scikitlearn, #TensorFlow, #PyTorch.
But it’s not just Python.
Torch for one started in #Lua.
#Julia has its scientific niche.
#Mojo promises Python-like syntax with C-level speed.

Formation Python pour la data science Formation et conseil data science, statistiques et analyse de données

🚀 Formation "Python pour la Data Science" – Automne 2025 🎯

📅 3 jours / 21 h
- 8–10 octobre
- 15–17 décembre 2025

📌 Inscrivez-vous dès maintenant pour la rentrée !
www.stat4decision.com/fr/formation...

#Python #DataScience #Formation #Jupyter #scikitlearn

Experiments in Supervised Learning Part 4 Random Musings on Technology

Part 4 of my supervised learning series is out!
How does model choice impact real-world results (and deployment headaches)? Plus show how bring the models into production via FastAPI & Docker.
agingcoder.com/posts/2025-0...
#AI #scikitLearn #MachineLearning #DataScience

Experiments in Supervised Learning Part 3 Random Musings on Technology

Part 3 of my Supervised Learning series is LIVE!
How do you take a naive model and tune it into something nearly production-ready?
Find out in my latest deep dive with real scikit-learn code & tips:
🔗 agingcoder.com/posts/2025-0...
#MachineLearning #scikitlearn #AI #Python

Understanding Random Forest using Python (scikit-learn)

Понимание случайного леса с помощью библиотеки scikit-learn

Случайный лес) - это мощный алгоритм машинного обучения, который может быть использован для классификации и регрессии, интерпретируем, и не требует масштабирования признаков. Вот как применить его.

#ai #news #scikitlearn

By setting shuffle=False, you maintain the correct chronological sequence crucial for accurate forecasting evaluation.

Have you encountered issues due to this overlooked setting? I'd love to hear your experiences below!

#TimeSeries #Forecasting #MachineLearning #DataScience #ScikitLearn