#FeatureEngineering

Scaling Feature Engineering Pipelines with Feast and Ray Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems

2 weeks ago

Scaling Feature Engineering Pipelines with Feast and Ray

Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems

Telegram AI Digest
#ai #featureengineering #rayproject

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AI и ML Новости

@ai-ru.at.thenote.app

2 weeks ago

Lag Features and Rolling Features in Feature Engineering

Особенности задержки и перемещённые функции в инженерии функций

Успех конвейеров машинного обучения зависит от разработки признаков как их фундаментальной основы. Два самых сильных метода обработки данных временных рядов — это лаговые признаки и …

Telegram ИИ Дайджест
#ai #featureengineering #news

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Lag Features and Rolling Features in Feature Engineering The success of machine learning pipelines depends on feature engineering as their essential foundation. The two strongest methods for handling time series data are lag features and rolling features, according to your advanced techniques. The ability to use these techniques will enhance your model performance for sales forecasting, stock price prediction, and demand planning tasks. […]

2 weeks ago

Lag Features and Rolling Features in Feature Engineering

The success of machine learning pipelines depends on feature engineering as their essential foundation. The two strongest methods for handling time series data are lag features and rolling fe…

Telegram AI Digest
#ai #featureengineering #news

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Matasoft

@matasoft.bsky.social

3 weeks ago

Try (Un)Perplexed Spready in Your Browser Try (Un)Perplexed Spready online: a pre-configured AI-powered spreadsheet in your browser with a free 4-hour sandbox, demo workbooks, and ready-to-use LLM, web search and taxonomy tools.

Analysts: tired of manually labeling text columns before modeling? (Un)Perplexed Spready lets AI formulas with prompts turn messy text into structured features in‑sheet.
matasoft.hr/qtrendcontro...
#DataScience #FeatureEngineering #LLM #AI

0 0 0 0

Matasoft

@matasoft.bsky.social

1 month ago

Try (Un)Perplexed Spready in Your Browser Try (Un)Perplexed Spready online: a pre-configured AI-powered spreadsheet in your browser with a free 4-hour sandbox, demo workbooks, and ready-to-use LLM, web search and taxonomy tools.

Ops teams: standardise entries, classify tickets and label entities directly in spreadsheets using AI formulas, not scripts. That’s the promise of (Un)Perplexed Spready.
matasoft.hr/qtrendcontro...
#Ops #AISpreadsheet #ProcessAutomation #DataScience #FeatureEngineering #AISpreadsheet #AI #LLM

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Matasoft

@matasoft.bsky.social

1 month ago

Data & analytics pros: use (Un)Perplexed Spready as a prompt‑driven feature generator for text spreadsheet columns—then export CSV into your ML or BI stack.
matasoft.hr/qtrendcontro...
#DataScience #FeatureEngineering #AISpreadsheet #SpreadsheetAI #PromptEngineering #Ollama #Spreadsheets #LLM #AI

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EkasCloud – Personalized Training Platform

@ekascloud.bsky.social

1 month ago

Post from EkasCloud Online Courses - YouTube Feature Engineering Best Practices A Guide for Data Scientists #FeatureEngineering #DataScience #MachineLearning #MLModels #DataPreprocessing #AI #DataScient...

Feature Engineering Best Practices A Guide for Data Scientists
www.youtube.com/post/UgkxvhZ...
#FeatureEngineering #DataScience #MachineLearning #MLModels #DataPreprocessing #AI #DataScientists #ModelAccuracy #DataTransformation #TechInsights #Analytics #BigData #MLBestPractices #EkasCloud

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EkasCloud – Personalized Training Platform

@ekascloud.bsky.social

1 month ago

Feature Engineering Best Practices: A Guide for Data Scientists
#FeatureEngineering #DataScience #MachineLearning #MLBestPractices #DataPreparation #DataPreprocessing #AI #ModelBuilding #BigData #Analytics #DataEngineering #MLModels #TechInsights #EkasCloud

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EkasCloud – Personalized Training Platform

@ekascloud.bsky.social

1 month ago

Feature Engineering Best Practices A Guide for Data Scientists Feature engineering is a cornerstone of effective machine learning. By transforming raw data into meaningful features, data scientists can improve model performance, interpretability, and generaliz...

Feature Engineering Best Practices A Guide for Data Scientists
www.ekascloud.com/our-blog/fea...
#FeatureEngineering #DataScience #MachineLearning #MLModels #DataPreprocessing #AI #DataScientists #ModelAccuracy #DataTransformation #TechInsights #Analytics #BigData #MLBestPractices #EkasCloud

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LAOMUSIC ARTS

@laomusicarts.bsky.social

2 months ago

Welcome to this course - Data Preparation, Feature Engineering, and Augmentation for AI Models Video Tutorial | LinkedIn Learning, formerly Lynda.com Join Dan Sullivan for an in-depth discussion in this video, Welcome to this course, part of Data Preparation, Feature Engineering, and Augmentation for AI Models.

LAOMUSIC ARTS 2025
presents

I just finished the course “Data Preparation, Feature Engineering, and Augmentation for AI Models” by Dan Sullivan!

Check it out:

www.linkedin.com/learning/dat...

#lao #music #arts #laomusicarts #ai #retrievalaugmentedgeneration #datapreparation #featureengineering

1 0 0 0

AI Daily Post

@aidailypost.com

3 months ago

Before you dive into deep learning, nail the basics—linear regression, decision trees, and solid feature engineering. Those core algorithms still drive real business value. Curious how they stack up against neural nets? Read on! #LinearRegression #DecisionTrees #FeatureEngineering

🔗

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Aleksis Pirinen

@aleksispi.bsky.social

3 months ago

This cup - with the @climateainordics.com logo and xmas colored border - will be the key feature with which to recognize me at EurIPS next week! Probably @mogren.ml too (thanks for ordering these!)

#FeatureEngineering

2 0 1 1

Bayo Adejare

@bayoadejare.bsky.social

3 months ago

𝟓/𝟓⁣
Data quality = AI quality. What's your biggest data transformation challenge?⁣
⁣
Like/Repost if you're building data pipelines! 🔁⁣
⁣
#DataEngineering #TimescaleDB #FeatureEngineering #MLOps

0 0 0 0

FreelanceBar.me

@freelancebarme.bsky.social

4 months ago

5 Advanced Feature Engineering Techniques with LLMs for Tabular Data - MachineLearningMastery.com In this article, you will learn practical, advanced ways to use large language models to engineer features that fuse structured (tabular) data with text for stronger downstream models.

With the rise of LLMs, traditional ML concepts like feature engineering may take a backseat. #MachineLearning #FeatureEngineering machinelearningmastery.com/5-advanced-feature-engin...

1 0 0 0

AI и ML Новости

@ai-ru.at.thenote.app

4 months ago

Prompt vs Feature Engineering: The Hidden Bridge Between Humans and Machines

Промпт-инжиниринг против инжиниринга признаков: Скрытый мост между человеком и машиной

И инжиниринг подсказок, и инжиниринг признаков служат одной и той же невидимой цели — преобразованию нечетких человеческих намерений во что-то понятное машин…

👉 Telegram ИИ Дайджест
#ai #featureengineering #news

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Prompt vs Feature Engineering: The Hidden Bridge Between Humans and Machines Both Prompt Engineering and Feature Engineering serve the same invisible purpose — turning messy human intent into something machines can understand.Feature engineering shapes data for training, while prompts shape instructions for inference.In an age where LLMs and ML models coexist, understanding their synergy is key: prompts can now generate features, and features can refine prompts.

4 months ago

Prompt vs Feature Engineering: The Hidden Bridge Between Humans and Machines

Both Prompt Engineering and Feature Engineering serve the same invisible purpose — turning messy human intent into something machines can understand.Featur…

👉 Telegram AI Digest
#featureengineering #llm #promptengineering

1 0 0 0

Hyperparameter Tuning and Feature Engineering: A Guide to Optimizing Machine Learning Models Achieving peak machine learning model performance hinges more on hyperparameter tuning and feature engineering than on modeling choices. These crucial, often overlooked, processes bridge the gap between mediocre and exceptional results. Effective optimization of these areas can lead to significant business advantages. Building a machine learning model is the easy part; making it reliable, performant, and cost-efficient in real-world applications is challenging. Under-optimized models incur hidden expenses such as wasted computational resources and subpar user experiences. They can also produce biased or unstable predictions, leading to missed commercial opportunities. In production, these problems manifest as direct financial losses or negative consequences for users. Therefore, focusing on hyperparameter tuning and feature engineering is essential for maximizing the value of machine learning initiatives.

5 months ago

Hyperparameter Tuning and Feature Engineering: A Guide to Optimizing Machine Learning Models

Achieving peak machine learning model performance hinges more on hyperparameter tuning and feature engineering than on modeling choices. These crucial,…

#featureengineering #hyperparameter #machinelearning

1 0 0 0

HackerNoon

@hackernoon.com

6 months ago

Stop Waiting: Make XGBoost 46x Faster With One Parameter Change

Speed up XGBoost training by 46x with one parameter change. Learn how GPU acceleration saves hours, boosts iteration, and scales to big data.
#featureengineering

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Soledad Galli, PhD

@solegalli.bsky.social

6 months ago

👉MICE is a powerful method for datasets with missing data across multiple variables.

Let this slide guide you through how it works.

#machinelearning #MICE #mlmodels #datascience #dataengineering #imputation #featureengineering

0 0 0 0

HackerNoon

@hackernoon.com

6 months ago

Building a Lambda-Style Feature Platform with GCP Native Services

Discover how we built a zero-ops, serverless feature engineering platform on Google Cloud using BigQuery, Dataflow, and Vertex AI. #featureengineering

1 0 0 0

FreelanceBar.me

@freelancebarme.bsky.social

6 months ago

https://machinelearningmastery.com/7-pandas-tricks-for-time-series-feature-engineering/

Enhancing time-series data for machine learning is crucial for effective models. #FeatureEngineering #TimeSeriesAnalysis machinelearningmastery.com/7-pandas-tricks-for-time...

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Jesus Castagnetto 🇵🇪 (@jmcastagnetto@mastodon.social)

@jmcastagnetto.bsky.social

6 months ago

"Basic Feature Engineering with #DuckDB"

#ML #FeatureEngineering

duckdb.org/2025/08/15/m...

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Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python 📘 Mastering Modern Time Series Forecasting (early access)The book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems. 💸 Pricing 🎉 Standard Edition Price: $40 | Minimum: $35Will increase to $80+ as content grows. A tremendous amount of work and expertise has gone into this book, which is designed to deliver exponential improvement to your forecasting skills, your company's bottom line and ROI, and your career. Forecasting is one of the most in-demand skills across nearly every industry today. 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 This Book Stands Out🔑 Forecasting models are only 5% of the equation.The other 95%? It’s the hard-earned knowledge of metrics, validation, deployment, failure modes, and real-world constraints — insights that are often missing or buried in internet noise and social media fluff.🔍 It starts with what actually matters: solid foundations.Learn how to properly evaluate forecasts, recognize when they're failing, and build with confidence — not on shaky assumptions, but on methods that stand up to real-world pressure.💎 You’ll also learn how to assess the forecastability of a time series — a critical step for managing your time, setting stakeholder expectations, and realistically estimating how far forecasting accuracy can be pushed before diminishing returns kick in.🧠 Built for understanding — not just coding.Go beyond black-box code. Grasp model mechanics and decision-making logic to truly understand how and why things work.💻 Clear, transparent, production-ready code.No obfuscation, no throwaway scripts. Every example is fully documented, reusable, and ready for real-world use.🔄 Continuously improved through real feedback.This is a living resource shaped by an active community of readers. Many improvements and additions come directly from their thoughtful feedback — and all readers get lifetime updates, including new chapters and bonus tools. Thank you to all contributors — your insights are recognized and appreciated in the book.📚 Comprehensive, real-world coverage.From classical time series models to deep learning and forecasting-specific transformers (FTSMs), the book covers a wide range — but always with a practical lens. Every method has been tested in production or validated against strong academic benchmarks. No fluff, just tools that work.📈 Real ROI — for your company and your career.Readers often see immediate improvements in model accuracy, interpretability, and stakeholder trust. No more silent failures or fragile production systems. This book helps you build forecasting solutions that earn trust, drive business results, and accelerate your career.✍️ 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.🎓 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 → maven.com/valeriy-manokhin/modern-forecasting-mastery📦 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 – $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.https://valeman.gumroad.com/l/MasteringModernTimeSeriesForecastingPro💸 New Pricing effective 16th June - grab your copy before price increase 🎉 Standard Edition Price: $45 | Minimum: $39Will increase to $80+ as content grows.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 real-world deployment?👉 Hit “Buy Now” and start mastering forecasting like never before.

7 months ago

🔓 Grab the book before the price increases this week:
👉 valeman.gumroad.com/...

#TimeSeries #MTF #FeatureEngineering #MachineLearning #Forecasting #DataScience #Python #SignalProcessing #GAF #CNN #DeepLearning #ViT #ActivityRecognition #ECG #AnomalyDetection

1 0 0 0

Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python 📘 Mastering Modern Time Series Forecasting (early access)The book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems. 💸 Pricing 🎉 Standard Edition Price: $40 | Minimum: $35Will increase to $80+ as content grows. A tremendous amount of work and expertise has gone into this book, which is designed to deliver exponential improvement to your forecasting skills, your company's bottom line and ROI, and your career. Forecasting is one of the most in-demand skills across nearly every industry today. 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 This Book Stands Out🔑 Forecasting models are only 5% of the equation.The other 95%? It’s the hard-earned knowledge of metrics, validation, deployment, failure modes, and real-world constraints — insights that are often missing or buried in internet noise and social media fluff.🔍 It starts with what actually matters: solid foundations.Learn how to properly evaluate forecasts, recognize when they're failing, and build with confidence — not on shaky assumptions, but on methods that stand up to real-world pressure.💎 You’ll also learn how to assess the forecastability of a time series — a critical step for managing your time, setting stakeholder expectations, and realistically estimating how far forecasting accuracy can be pushed before diminishing returns kick in.🧠 Built for understanding — not just coding.Go beyond black-box code. Grasp model mechanics and decision-making logic to truly understand how and why things work.💻 Clear, transparent, production-ready code.No obfuscation, no throwaway scripts. Every example is fully documented, reusable, and ready for real-world use.🔄 Continuously improved through real feedback.This is a living resource shaped by an active community of readers. Many improvements and additions come directly from their thoughtful feedback — and all readers get lifetime updates, including new chapters and bonus tools. Thank you to all contributors — your insights are recognized and appreciated in the book.📚 Comprehensive, real-world coverage.From classical time series models to deep learning and forecasting-specific transformers (FTSMs), the book covers a wide range — but always with a practical lens. Every method has been tested in production or validated against strong academic benchmarks. No fluff, just tools that work.📈 Real ROI — for your company and your career.Readers often see immediate improvements in model accuracy, interpretability, and stakeholder trust. No more silent failures or fragile production systems. This book helps you build forecasting solutions that earn trust, drive business results, and accelerate your career.✍️ 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.🎓 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 → maven.com/valeriy-manokhin/modern-forecasting-mastery📦 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 – $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.https://valeman.gumroad.com/l/MasteringModernTimeSeriesForecastingPro💸 New Pricing effective 16th June - grab your copy before price increase 🎉 Standard Edition Price: $45 | Minimum: $39Will increase to $80+ as content grows.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 real-world deployment?👉 Hit “Buy Now” and start mastering forecasting like never before.

7 months ago

🔓 Grab the book before the price increases:
👉 valeman.gumroad.com/...

#TimeSeries #RecurrencePlot #Forecasting #FeatureEngineering #DataScience #RQA #Chaos #Python #MachineLearning #SignalProcessing

1 0 0 0

#timeseries #machinelearning #forecasting #datascience #featureengineering #python #deeplearning #ML #data

7 months ago

1 0 0 0

#TimeSeries #ComputerVision #GAF #QuantFinance #CNN #VisionTransformer #Forecasting #FeatureEngineering #Python #DataScience #MachineLearning #DeepLearning #Finance #JPmorgan #AlgoTrading

7 months ago

2 0 0 0

FreelanceBar.me

@freelancebarme.bsky.social

7 months ago

https://blog.stackademic.com/scientific-writing-4-statistical-analysis-reporting-in-data-science-best-practices-and-examples-9542b70f5097?source=rss----d1baaa8417a4---4 Scientific Writing for Data Science — Part 4/18

Discover the importance of feature scaling in data preprocessing for better model performance and results! Learn more about #datascience and #featureengineering in this insightful article. Read more on Stackademic! blog.stackademic.com/scientific-writing-4-sta...

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Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python 📘 Mastering Modern Time Series Forecasting (early access)The book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems. 💸 Pricing 🎉 Standard Edition Price: $40 | Minimum: $35Will increase to $80+ as content grows. A tremendous amount of work and expertise has gone into this book, which is designed to deliver exponential improvement to your forecasting skills, your company's bottom line and ROI, and your career. Forecasting is one of the most in-demand skills across nearly every industry today. 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 This Book Stands Out🔑 Forecasting models are only 5% of the equation.The other 95%? It’s the hard-earned knowledge of metrics, validation, deployment, failure modes, and real-world constraints — insights that are often missing or buried in internet noise and social media fluff.🔍 It starts with what actually matters: solid foundations.Learn how to properly evaluate forecasts, recognize when they're failing, and build with confidence — not on shaky assumptions, but on methods that stand up to real-world pressure.💎 You’ll also learn how to assess the forecastability of a time series — a critical step for managing your time, setting stakeholder expectations, and realistically estimating how far forecasting accuracy can be pushed before diminishing returns kick in.🧠 Built for understanding — not just coding.Go beyond black-box code. Grasp model mechanics and decision-making logic to truly understand how and why things work.💻 Clear, transparent, production-ready code.No obfuscation, no throwaway scripts. Every example is fully documented, reusable, and ready for real-world use.🔄 Continuously improved through real feedback.This is a living resource shaped by an active community of readers. Many improvements and additions come directly from their thoughtful feedback — and all readers get lifetime updates, including new chapters and bonus tools. Thank you to all contributors — your insights are recognized and appreciated in the book.📚 Comprehensive, real-world coverage.From classical time series models to deep learning and forecasting-specific transformers (FTSMs), the book covers a wide range — but always with a practical lens. Every method has been tested in production or validated against strong academic benchmarks. No fluff, just tools that work.📈 Real ROI — for your company and your career.Readers often see immediate improvements in model accuracy, interpretability, and stakeholder trust. No more silent failures or fragile production systems. This book helps you build forecasting solutions that earn trust, drive business results, and accelerate your career.✍️ 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.🎓 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 → maven.com/valeriy-manokhin/modern-forecasting-mastery📦 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 – $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.https://valeman.gumroad.com/l/MasteringModernTimeSeriesForecastingPro💸 New Pricing effective 16th June - grab your copy before price increase 🎉 Standard Edition Price: $45 | Minimum: $39Will increase to $80+ as content grows.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 real-world deployment?👉 Hit “Buy Now” and start mastering forecasting like never before.

7 months ago

🔓 After this chapter drops, the book price goes up.
Get your copy now and lock in lifetime access at the current price.

valeman.gumroad.com/...

#TimeSeries #Forecasting #FeatureEngineering #ML #DataScience #DeepLearning #Kaggle

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