Machine Learning

Are you interested in starting or returning to Machine Learning? This book offers a concise, straightforward view into the field and fundamental techniques that see use even in high-level career roles. Through a combination of examples, math and coding projects, the book will grow the reader's confidence in being a Machine Learning practitioner.

Building generative AI application is not only about LLM choice and prompt engineering, but also about the well-architected cloud solution.

Mastering Advanced Time Series Forecasting in Python is the definitive sequel to the #1 forecasting bestseller. Designed for practitioners who want to go beyond ARIMA and basic ML, this book takes you deep into probabilistic forecasting, hierarchical coherence, and cutting-edge foundation models—backed by production-ready Python code. Learn how to assess forecastability, build scalable pipelines, quantify uncertainty, and deploy systems that deliver real business impact. Written by a globally recognized expert whose methods power multimillion-dollar decisions, this is the practical, honest, and advanced guide every data scientist, ML engineer, and quantitative professional needs to master modern forecasting.

Build a real AI platform on OpenShift, not just “another Kubernetes cluster.” This guide walks you through air-gapped installs, Quay mirroring, GPUs, InfiniBand, GitOps, and benchmarking—so platform and SRE teams can deliver a secure, observable, high-performance OpenShift AI environment that app teams actually want to use.

This reference volume consists of revised, edited, cross-referenced, and thematically organized articles from the Software Diagnostics and Observability Institute and the Software Diagnostics Library (former Crash Dump Analysis blog) about software diagnostics, root cause analysis, debugging, crash and hang dump analysis, and software trace and log analysis written from 15 April 2024 to 14 November 2025.

PLEASE NOTE THAT THIS IS NOT THE FULL BOOK BUT JUST CHAPTER 1 and 2 because a simple topic like Simple Linear Regression became 20+ pages long. As a full-time frontend architect, I'm writing and releasing this book chapter by chapter, at a pace that fits around my day job and life. So, think of this as a journey we're taking together, one concept at a time. REST OF THE CHAPTERS WILL BE AVAILABLE AT REGULAR INTERVALS. So keep me motivated by subscribing :)

Most AI systems fail in production. They chain prompts together and call it "agentic". They collapse under real-world pressure. The gap isn't the technology – it's missing infrastructure. This book bridges that gap. Learn the architecture patterns and infrastructure to build autonomous AI systems that work at scale. Master what distinguishes real agents from chatbots with tools. Deploy with confidence. Stop building demos. Start shipping production agentic AI.

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.

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.

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.

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.

หนังสือเล่มนี้เป็นคู่มือฉบับกระชับพร้อมภาพประกอบ สำหรับผู้ที่อยากเข้าใจการทำงานภายในของแบบจำลองภาษาขนาดใหญ่ ในบริบทการสัมภาษณ์ ทำโครงการ หรือเพื่อสนองตอบความใคร่รู้ของตนเอง

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.

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

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