Books

Not just for mathematicians and detectives! Learn the basics of logic and its many applications, including advanced testing, constraint solving, function composition, and code proofs. Over 50 exercises and 20 sample programs are included. No prior math background required!

This book supplements the DM for CS Specialization at Coursera and contains many interactive puzzles, autograded quizzes, and code snippets. They are intended to help you to discover important ideas in discrete mathematics on your own. By purchasing the book, you will get all updates of the book free of charge when they are released.

If you are able to contribute, it will go to support OpenIntro (not the author), a US-based nonprofit working to spread open materials, e.g. by providing desk copies to instructors considering this text. This listing is in collaboration with the textbook's author, Jim Hefferon. Linear Algebra's official websitePaperbacks are $22openintro.org

This textbook accompanies a year-long Discrete Mathematics course for Computer Science and AI students, covering classical topics such as combinatorics, graph theory, probability, logic, and set theory. It emphasizes applications across computer science and complements the standard curriculum with advanced topics in each chapter.

The most successful calculus textbook in history — now in English for the first time.

The most successful algebra textbook in history — now in English for the first time.

The most successful algebra textbook in history — now in English for the first time.

Leanpub revenue supports OpenIntro. OpenIntro is a US-based nonprofit that provides textbook services to help increase adoption of OER textbooks and save students money. These textbook services are fully financed by Leanpub contributions on supported books. APEX Calculus website: apexcalculus.comOpenIntro website: openintro.org

Most people think they are bad at math. In reality, they were never taught arithmetic properly.This book is a modern English edition of Arithmetic by Alexander P. Kiselev—the text that formed the backbone of mathematical education in Russia and USSR for over a century and helped produce generations of exceptionally strong mathematicians, scientists, and engineers.Unlike modern textbooks that prioritise shortcuts, visuals, and lowered expectations, Kiselev builds arithmetic logically, systematically, and rigorously. Every method is explained. Every operation has meaning. Exercises are carefully sequenced to develop real understanding—not rote pattern-following.This book does not promise “easy math”. It promises something better: clarity, confidence, and competence.Whether you are a student, a parent, a tutor, or an adult rebuilding fundamentals, this book will change how you understand arithmetic—and why so much later mathematics suddenly becomes easier.

Schluss mit Mathe-Frust: In 56 Tagen zum selbstbewussten Kopfrechen-Profi!Kennst du das auch? Bei jeder kleinen Rechenaufgabe im Alltag wandert der Blick sofort zum Smartphone. Doch spätestens in der Klassenarbeit – wenn der Taschenrechner im Rucksack bleiben muss – bricht oft Stress aus. Der hilfsmittelfreie Teil sorgt bei vielen Schülern für Blockaden und Frust.Das muss nicht sein! Mathe darf leicht sein – wenn man die richtigen Abkürzungen kennt.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

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.

A complete foundation for Statistics, also serving as a foundation for Data Science, that introduces inference using randomization and simulation while covering traditional methods. Leanpub revenue supports OpenIntro, so we can provide free desk copies to teachers interested in using our books in the classroom. More resources: openintro.org.

Lemurs can represent approximate numerical values and have numerical rule-making abilities - so the author thought, "Well if a lemur can do that, why can't I do that too?" This book is an attempt at making practical probabilistic concepts much easier to grasp and internalize by tapping into the highly-visual, primitive part of our brains.