Hola red, espero que hayan tenido un bonito fin de semana! 😃 Les vengo a invitar al taller práctico que impartiremos el próximo mes en el #DataDay sobre feature stores y MLOps. Esperamos que les ayud...

Master Conformal Prediction and become the expert who delivers trustworthy, risk-aware ML in production.

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Prepare for Cloudera CDP-6001 with interactive quiz questions and practice tests. Evaluate your exam readiness and strengthen your certification skills.

THE QUICK BRIEF The Core Technology: Prompt engineering is the systematic practice of designing and refining input instructions to guide large language model (LLM) behavior without modifying model weights. Key Performance Metrics: Accuracy Impact: Up to 76-point variance in model accuracy based solely on prompt structure Security Improvement: Security-focused prompts reduce code vulnerabilities by 56% (GPT-4o) Cost Range: GPT-3.5: $0.50-$1.50/M input tokens; GPT-4o: $2.50/M input, $10/M output; GPT-4: $30/M input, $60/M output…

🛒 Pre‑Order Details Incremental chapter release: Chapters are released gradually. As content drops, both the value and price climb over time. Lifetime updates included: By preordering now, you’ll receive every current and future update for the book at no extra cost. By Valeriy Manokhin, PhD, MBA, CQF “CatBoost is not just underrated—it’s objectively better.”This book shows you why, with the science and the code to prove it. 💸 Pricing 🎉 Launch Price: $30 | Minimum: $25Will increase to $60+ as content grows. As the content continues to grow, if you find value in it—or simply want to support the project—you're welcome to contribute whatever it’s worth to you ❤️. 🧠 Why CatBoost?There’s a preponderance of scientific evidence that CatBoost consistently and significantly (20%+ according to TabArena outperforms XGBoost, and LightGBM on real-world tabular data.It's faster in inference, easier to tune, and built from the ground up for categorical features—without the usual preprocessing hacks.Despite this, CatBoost remains one of the most underused tools in machine learning. This book fixes that.🧪 Backed by research, benchmarks, and production experience📈 Practical, readable, hands-on for working data scientists🔬 Linked to the open-source repo: Awesome CatBoost🔍 What You’ll Learn Core architecture: how CatBoost works under the hood Hands-on modeling: end-to-end tabular ML pipelines Categorical encoding: no more label/one-hot hacks Overfitting detection: built-in, automated safeguards Evaluation strategies: cross-validation the CatBoost way Interpretability: SHAP, feature importance, monotonic constraints Bonus: Time series with CatBoost + quantile & uncertainty modeling 📘 Scope & Depth: More than Just Boosters Mastering CatBoost covers: Not just classification, but regression, ranking, time series, and even quantile/uncertainty models Deep dive into categorical feature handling (CatBoost’s core advantage) Native overfitting detection, monotonic constraints, and interpretability tools all built-in and tuned for tabular workflows 🏗️ Under-the-Hood Architecture & Scientific Advantages Harrison’s book provides intuition and tuning advice, with code examples and deployment workflows . Mastering CatBoost delves into: Ordered boosting, symmetric trees, and smoothed target statistics — explaining why CatBoost handles categorical variables without leakage Scientific benchmarks consistently show CatBoost outperforming XGBoost and LightGBM on real-world tabular datasets Includes newer capabilities like GPU optimizations, quantization, and ONNX export 🧩 Interpretability & Safeguards Native overfitting detection, eliminating guesswork Built-in per-feature importance, interaction, and partial dependence tools Monotonic constraints tuned specifically for CatBoost internals 🎯 The Verdict Mastering CatBoost goes far beyond: In technical depth (architecture + categorical handling) Applied scope (classification, regression, ranking, forecasting) Deployment readiness (quantization, ONNX, real-world pipelines) Support materials (Awesome_CatBoost repo, notebooks, domain-specific chapters) 👨‍💻 Who Is This For?This book is designed for: Machine learning engineers using tabular datasets Data scientists tired of endless hyperparameter tuning Students or researchers who’ve hit limits with XGBoost or sklearn Practitioners who want to move fast from data to insight If you like fast iteration, fewer bugs, and state-of-the-art tabular models, this book is for you.📦 What You Get📥 Instant access to the book — start reading immediately.🔄 Free updates — including new chapters, bug fixes, and bonus content.💬 Exclusive access to the private Discord community — connect with fellow readers, get additional materials, early bonuses, special discounts, and join live events with the author.🔓 Pro Edition Bonus Pack (Early Access – $60) 🔥🔥🔥 Includes everything above, plus:✅ Premium Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Tabular Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.📚 Also by the AuthorMastering Modern Time Series ForecastingThe book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems.Learn more → MasteringModernTimeSeriesForecasting⚡ Ready to Master the Best Tabular Model in ML?CatBoost isn’t just another gradient booster.It’s the most underappreciated breakthrough in machine learning—and you’re about to master it.👉 Grab your copy now and start building faster, better models with less tuning.

🔥 Pro Edition: Mastering Modern Time Series Forecasting (Early Access) The elite version of the book — trusted by data science leaders in 100+ countries.Unlock the premium toolkit behind today’s most powerful forecasting systems.🚀🚀🚀 New: Pro Edition Now Available — $65 (USD) 🔥🔥🔥Includes everything in the standard edition plus: premium forecasting templates, cheat sheets, extended case studies, behind‑the‑scenes notebooks, model tuning toolkits, and access to live Q&A + AMA sessions with the author.⚠️ Final price of Pro Edition will rise to $150+ at book completion.See 📦 What You Get and 💸 Pricing for full details.The Definitive Guide to Statistical, Machine Learning & Deep Learning Models in PythonLet’s face it — most forecasting books fall short in one way or another. Many are outdated, overly simplistic, or written by authors without hands-on experience building real-world forecasting systems. While a few classics stand out, they tend to focus solely on traditional methods — often geared toward beginners (like Hyndman) or buried under a thousand pages of dense theory (like Hamilton).Plenty of resources cover classical techniques or dive deep into theoretical details, but they rarely go the distance in offering practical guidance — especially when it comes to modern tools like machine learning, deep learning, and production-ready systems.Even some newer books that try to bridge this gap often end up skimming the surface. They offer high-level overviews without the depth or practical insights needed to build forecasting solutions that actually work in real-world environments. Worse, they often skip foundational concepts entirely — making it easy for readers to follow copy-paste recipes without understanding what’s really going on. The result? Fragile systems that seem fine in testing, but fall apart under real-world pressure.If you’ve ever felt confused by unexplained code, frustrated by missing fundamentals, or unsure how to move from theory to application — you’re not alone.This book is different.Mastering Modern Time Series Forecasting is a complete, hands-on guide that blends statistical, machine learning, and deep learning approaches — all grounded in real-world experience. It’s built to be clear, thorough, and practical from start to finish. Whether you're just beginning your journey or leading forecasting initiatives at scale, this book gives you the tools, understanding, and workflows to build systems that make a real impact — and actually hold up in production.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals. Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 30 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.🎓 Want a Live, Interactive Learning Experience?Pair this book with the Modern Forecasting Mastery course on Maven.Join live cohort sessions with Valeriy, get direct feedback, and build models with peers.Next cohort opens soon → maven.com/valeriy-manokhin/modern-forecasting-mastery🔍 What You'll Learn📘 Core Forecasting FoundationsGrasp what forecast accuracy really means, master model validation strategies, and sidestep common pitfalls that trip up even experienced practitioners.📈 Classical Models, Done RightIn-depth, modern takes on ARIMA, Exponential Smoothing, and other classical statistical models — with code you can actually use.🤖 Machine Learning for Time SeriesBuild feature‑rich forecasts using state‑of‑the‑art ML techniques that go far beyond black‑box models.🧠 Deep Learning & TransformersExplore powerful deep learning architectures, including Transformer‑based models — all with clear, readable PyTorch code.📊 FTSMs – Foundational Time Series ModelsExplore large, domain‑general pre‑trained models (“GPT for time series”) with full implementation insights.🎯 Probabilistic & Interpretable ForecastingMove beyond point forecasts using techniques like conformal prediction, SHAP, attention, and explainability tools.📊 Real‑World Case StudiesRetail, energy, finance — apply what you learn to live datasets and market scenarios.👥 Who It’s For Data Scientists & ML Engineers building real-world forecasting systems Analysts & Developers looking for practical, hands-on code and frameworks Students, Educators & Researchers seeking a modern and curriculum-ready reference Demand Planners & Business Strategists translating forecasts into action and ROI 🧠 Why This Book Stands Out🔑 Forecasting models are just 5% of what it takes to build successful forecasting systems.The remaining 95% — the hard-won knowledge about metrics, validation, deployment, failure modes, and real-world constraints — is either unavailable or drowned out in a sea of internet noise and social media fluff.🔍 It starts with what actually matters: solid foundations.That means learning how to evaluate forecasts properly, understand when they're broken, and build with confidence — not on shaky assumptions, but on methods that hold up under real-world pressure.🧠 Focuses on understanding, not just codingUnderstand model mechanics and decision-making—not black-box training code.💻 Fully documented, transparent codeNo obfuscation. Every example is explainable, reusable, and production-ready.🔄 Continuously improved with reader feedbackThis is a living resource, shaped by an ongoing review process involving a wide community of readers who provide thoughtful, real-world feedback. Many improvements, clarifications, and additions come directly from this collaboration. Thank you to all the reviewers — your contributions are acknowledged and appreciated in the book. Readers receive lifetime updates, including new chapters and bonus tools.📚 Comprehensive, real-world coverageFrom classical statistical models to deep learning and forecasting-specific transformers (FTSMs), this book covers it all — with a focus on what actually works. Every method included has been battle-tested in real-world projects or validated against robust academic benchmarks. No esoteric fluff — just practical tools and approaches that deliver results in production.📈 Real ROI — for your company and your careerReaders consistently report fast, tangible improvements in model accuracy, interpretability, and stakeholder confidence — often within weeks. No more forecasting models that silently fail or production systems that collapse under pressure. This book helps you build solutions that earn trust, drive business impact, and advance your career — not frustrate clients or burn out data science teams.📦 What You Get Instant download of the full book All code examples, datasets, and notebooks Free lifetime updates (new chapters, bug fixes, bonus content) Exclusive early access to upcoming bonus chapters & live Q&A with the author 🔓 Pro Edition Bonus Pack (Early Access – $65) Includes everything above, plus:✅ Premium Forecasting Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Forecast Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.💸 Pricing🎉 Standard Edition Price: $40 | Minimum: $35Will increase to $80+ as content grows.🚀 Pro Edition Early Access: Price: $70 | Minimum: $35Includes the full book + Premium Pack.✅ Lock in now—price will rise to $150+ at full release.If you find value or simply want to support the project, feel free to pay what it’s worth to you ❤️Ready to take your forecasting skills from stats to neural nets—and from theory to high-impact deployment?👉 Hit Buy Now, and if you want structured support, check out the course atmaven.com/valeriy-manokhin/modern-forecasting-mastery

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