Artificial Intelligence

Build a complete data pipeline from scratch — no cloud signup needed. Fifteen chapters covering ingestion, loading, data modeling, dbt, Airflow, data quality, streaming, observability, and performance optimization. Everything runs locally with Python and DuckDB.

Become a better data scientist by understanding different modeling mindsets.

An agent's answer can look right while its actions are wrong. Learn to evaluate tasks, traces, tool calls and changed state with reproducible harnesses, tested graders and regression gates.

Learn Claude Code by building real projects. This hands-on companion turns the Claude Code Masterclass workshop into a practical self-paced guide for planning, coding, testing, reviewing, refactoring, and shipping software with AI.

Using AI to code is easy. Using it without losing control — not so much.By week three, your project stops moving. The agent forgets decisions it made ten days ago. A change in auth breaks the dashboard. You spend more time re-explaining context than writing features. The code works, but only you know why — and you're not even sure you remember all of it.That's vibe coding hitting its ceiling.Spec-Driven Development is the method that replaces the chaos with a spec the AI actually executes. Not a ceremonial document. A working artifact: PRD, issues, tests, code — all traceable, all connected, all in the right order.This book shows you:How to grill your own idea before writing a single promptHow to write a PRD the AI won't misinterpretThe 7 phases that turn an idea into working softwareHow to use GitHub SpecKit and openSpec (and when not to)How to work this way in a team without slowing downThe 5 anti-patterns that destroy every spec22,000 words. 13 chapters. 5 appendices with ready-to-copy templates.No theory dumps. No filler. Just the method.

Aprende los conceptos básicos del Machine Learning y avanza poco a poco con teoría y divertidos ejercicios prácticos en Python a niveles intermedios y avanzados hasta llegar al Deep Learning.Tu camino para convertirte en un Científico de Datos comienza aquí

What You Will LearnBy the end of this book, you will be able to:1.     Understand the core concepts of unsupervised learning and how it differs from supervised learning.2.     Preprocess and prepare datasets for clustering, including scaling, normalization, and handling outliers.3.     Implement popular clustering algorithms in Python, tuning parameters for optimal results.4.     Evaluate clustering performance using both internal and external metrics.5.     Apply clustering techniques to real-world problems such as customer segmentation, anomaly detection, and image grouping.6.     Work with high-dimensional data and understand techniques to reduce dimensionality while preserving patterns.7.     Use advanced clustering techniques to solve complex data grouping problems in large datasets.8.     Develop ethical awareness of privacy, bias, and fairness in AI applications. Benefits After Studying This Book For Students ·        Gain strong theoretical foundations in machine learning without supervision.·        Prepare for academic exams, assignments, and competitive exams like UGC NET, GATE, and data science interviews.·        Build portfolio-worthy projects to showcase in internships or job applications. For Job Seekers and Professionals ·        Learn industry-relevant clustering algorithms used in AI, marketing, healthcare, and cybersecurity.·        Enhance data analysis and problem-solving skills to stand out in interviews for roles such as Data Scientist, Machine Learning Engineer, or Business Analyst.·        Understand how to integrate clustering techniques into business solutions for better decision-making. For Researchers and Innovators ·        Explore cutting-edge clustering methods and hybrid models for high-dimensional and big data scenarios.·        Gain insights into current trends and future research opportunities in unsupervised learning.·        Leverage clustering techniques for research publications, AI prototypes, and academic projects.

UPDATE: Please don't pay for this!! It's free. Please just take it for free. I'd much rather give it away. Even when it's done, it'll still be free. I'm 100% NOT trying to make money on this. Tell your friends, and be sure to read it. Your applications lives and runs in more places - MCP, desktop applications, browser-based application, data integration pipelines, gRPC, browsers, etc. - than ever. Can your security framework keep up? If you're using Spring Security, then it definitely can, and this book - by Spring Developer Advocate Josh Long - will show you how.

Agentic AI is everywhere, but what does it actually mean? This book cuts through the hype and explains how AI agents work, how they are built and where they can go wrong. Starting from the basics, it gives you a clear, practical understanding of the technology and what it takes to trust an agent in the real world.

AI agents are only as good as the loops behind them. This book shows you how to build systems that plan, act, evaluate and improve with every step. Learn the patterns, frameworks and engineering practices behind reliable, self-correcting agents that solve real problems in production.

FOCUS Architecture presents an opinionated architectural approach for creating simple, organized, and maintainable systems. Instead of multiplying layers and abstractions, the book demonstrates how to structure applications around four components—View, Orchestrator, Use Case, and Repository—thereby reducing the cost of change and simplifying the work for both developers and AI tools.

A scenario-based study guide for the Claude Certified Architect Foundations (CCAR-F) exam. Learn to reason like an architect across all five domains, then prove it on two full practice exams.

Tired of memory bottlenecks? It’s time to build AI with the speed and safety of Rust. Learn to bypass standard abstractions and achieve raw performance at the hardware level. Detailed architectural diagrams and expert exercises for modern high-speed AI stacks. Stop compromising on performance—start building the future of AI engineering today.

"The AI confirmed X for me" used as proof of X. Outputs that sound brilliant but don't hold up to a severe re-reading. A three-page "AI policy" that nobody reads. Sound familiar? Thinking with LLMs, the Right Way is the system of critical thinking applied to LLMs: the Thinking-With Triangle (Intent / Adversary / Editor), the four meta-decisions of governance, the Socratic and adversarial practices for investigating and verifying. Not prompt engineering: the method for not letting yourself be mirrored.

A practical guide to fine-tuning Large Language Models (LLMs), offering both a high-level overview and detailed instructions on how to train these models for specific tasks.Get the paperback version here. Get the Kindle version here.