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Category: "Machine Learning"

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  1. The Modern Guide to AI-Powered Web Scraping and Automation
    The Modern Guide to AI-Powered Web Scraping and Automation
    Hands-On Strategies for Stealth, Structured, and AI-Driven Web Scraping
    Wasi

    Discover how modern web scraping goes beyond simple scripts. Learn to extract, automate, and transform data from dynamic websites using Python, Playwright, undetected-chromedriver, Chrome DevTools MCP, and AI tools. Packed with hands-on examples and expert tips, this book shows you how to build ethical, scalable, and intelligent scraping workflows that turn raw web content into actionable insights.

  2. Neural Networks and Adaptive Control
    Neural Networks and Adaptive Control
    AN ONLINE MACHINE LEARNING PERSPECTIVE
    César Antonio López Segura

    This book presents a modern approach to system identification and adaptive control through the lens of online machine learning. It bridges theory and practice, guiding readers from classical linear control to advanced nonlinear adaptive methods with MATLAB examples. Designed for students, researchers, and engineers, it provides the knowledge and tools to design intelligent control systems for real-world applications.

  3. Generative AI for Science
    Generative AI for Science
    A Hands-On Guide for Students and Researchers
    J. Paul Liu

    Bridge AI and science with this hands-on guide. Whether you're a researcher learning ML or an engineer entering scientific applications, build real systems across chemistry, biology, physics & climate. Master Transformers, Diffusion Models & GNNs for scientific discovery. 500+ pages, 50+ Colab notebooks. Design molecules, predict proteins, accelerate climate models—all hands-on, zero setup required.

  4. Mastering Forecasting Metrics & Accuracy: For Data Science and Beyond

    Mastering Forecasting Metrics & Accuracy: For Data Science and BeyondForecasting models are only as good as the metrics used to measure them. Yet many teams still rely on outdated or misleading measures like MAPE. This book is the first comprehensive, practitioner-friendly guide dedicated entirely to forecast evaluation metrics — blending clear theory, Python recipes, and real-world case studies.Learn how to avoid common pitfalls, measure bias, handle intermittent demand, and apply advanced metrics like MASE, RMSSE, CRPS, pinball loss, and calibration scores. Each chapter includes formulas, code, and visuals to make concepts easy to apply.Perfect for data scientists, ML engineers, analysts, researchers, and industry professionals in retail, finance, and energy. No heavy math required.Living book: buy once, get free lifetime updates.Measure what matters.

  5. Temporal Aware AI memory: Why time is a key in a memory
    Temporal Aware AI memory: Why time is a key in a memory
    Why is time all you need?
    Volodymyr Pavlyshyn

    So how do you make an AI agent and conversational agent understand time? How does time shape attention? How is time important for the context engine? You will learn how to add time to knowledge graphs, how time and causality drive context, and how to make the knowledge graphs that are used for AI memory time-aware.

  6. Machine Learning for C# Developers Made Easy
    Machine Learning for C# Developers Made Easy
    Build smart applications with ML.NET
    Fiodar Sazanavets

    Helping C# and .NET developers to learn how to do machine learning and become highly sought-after (and well-paid) AI engineers. No prior experience of ML required!

  7. Super Study Guide: ตัวแปลงและแบบจำลองภาษาขนาดใหญ่

    หนังสือเล่มนี้เป็นคู่มือฉบับกระชับพร้อมภาพประกอบ สำหรับผู้ที่อยากเข้าใจการทำงานภายในของแบบจำลองภาษาขนาดใหญ่ ในบริบทการสัมภาษณ์ ทำโครงการ หรือเพื่อสนองตอบความใคร่รู้ของตนเอง

  8. The Hundred-Page Guide to the Core Mathematics of Machine Learning
    The Hundred-Page Guide to the Core Mathematics of Machine Learning
    A Practical Introduction to Linear Algebra, Calculus, and Statistics with Python and NumPy
    PR

    Demystify the mathematical foundations of machine learning with this concise, hands-on guide. Dive into the essentials of linear algebra, calculus, and statistics, specifically tailored for practical application with Python and NumPy. No more wading through dense textbooks – get the core concepts you need to build intelligent systems, fast.

  9. Introduction to AI
    Introduction to AI
    Fundamentals, Intuition, and a Simple PyTorch Project
    Elliot Farrow

    You’ll get a clear, beginner-friendly introduction to AI and machine learning, explanations of neural networks, data, and training processes, practical insights into how AI really works (without hype) and a hands-on PyTorch project to build your own small AI

  10. Probabilistic Forecasting with Conformal Prediction in Python
    Probabilistic Forecasting with Conformal Prediction in Python
    The Practical Guide to Uncertainty Quantification for Data Science, Machine Learning, and Forecasting
    Valery Manokhin

    Turn uncertainty into a competitive advantage with probabilistic forecasting and Conformal Prediction.

  11. The inner workings of Large Language Models
    The inner workings of Large Language Models
    how neural networks learn language
    Roger Gullhaug

    I wanted to understand how ChatGPT and other large language models (LLMs) really work, so I read a lot of books, watched YouTube videos, asked hundreds of questions, and wrote it all down. This book is the result. If you want to understand how large language models like ChatGPT actually work, from tokens and vectors to transformers and training, this book will explain it in a clear, approachable way.

  12. AGI for Business Leaders: Transforming Operations and Strategy for Billion-Dollar Growth
    AGI for Business Leaders: Transforming Operations and Strategy for Billion-Dollar Growth
    **Unlocking the Power of Artificial General Intelligence for Sustainable Growth and Billion-Dollar Success**
    Dr. johnson krish

    Unlock the power of Artificial General Intelligence (AGI) to revolutionize your business operations and strategy. This comprehensive guide explores how AGI can drive innovation, streamline processes, and enhance decision-making to help you build a multi-billion-dollar company. From optimizing customer experience and marketing to fostering sustainable growth, this book provides a step-by-step roadmap for leveraging AGI to achieve long-term success. Whether you're a business leader, entrepreneur, or strategist, discover how AGI can be your ultimate tool for competitive advantage in today’s fast-paced market. Get ready to transform your business and shape the future of your industry!

  13. Mastering CatBoost: The Hidden Gem of Tabular AI
    Mastering CatBoost: The Hidden Gem of Tabular AI
    Harness the Power of CatBoost for Tabular Data and Beyond
    Valery Manokhin

    Unlock the full potential of CatBoost — a powerful gradient boosting library built for structured/tabular data and still underused in practice.In Mastering CatBoost: The Hidden Gem of Tabular AI, you’ll learn how to take advantage of CatBoost’s key strengths: native categorical feature handlingstrong accuracy, and fast inference — without brittle preprocessing pipelines.Written for data scientists, ML engineers, and applied researchers, the book covers:Real-world use cases and end-to-end workflowsPractical tuning strategies and diagnosticsInterpretability with SHAP, feature importance, and constraintsDeployment-minded best practices and common failure modesWhether you’re new to CatBoost or ready to go deeper, this is a clear, practical guide to building high-performance tabular models.Early Access is available now, with ongoing updates leading to the full release in 2026.

  14. Data Careers 101: From Math to Machine Learning
    No Description Available
  15. Python Data Science Cookbook
    Python Data Science Cookbook
    Practical solutions across fast data cleaning, processing, and machine learning workflows with pandas, NumPy, and scikit-learn
    GitforGits | Asian Publishing House

    I wrote this cookbook to save you time troubleshooting and more time discovering insights. These recipes tackle the literal problems you'll face—mismatched keys, shape errors, memory leaks, rate limits—so that each step builds toward a smooth, automated workflow.