A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+

Simple concepts about up-and-coming science and technology to kick-start your future genius! Machine Learning for Babies & Toddlers is a great way to introduce basic concepts about machine learning, an exploding field that will be like electricity to our future generation. Familiarize your little one with what machines are and they can learn, just like us! The colorful, beautiful, and visually stimulating illustrations encourage the child's sense of wonder and curiosity (and might stimulate your senses too)! Levels of learning: Level 1 baby basics in black text and Level 2 in purple text for toddlers to build on. Look for other books by Tinker Toddlers(TM) Artificial Intelligence for Babies & Toddlers and Solar System for Babies & Toddlers

Fans of Chris Ferrie's ABCs of Economics, ABCs of Space, and Organic Chemistry for Babies will love this introduction to neural networks for babies and toddlers! Help your future genius become the smartest baby in the room! It only takes a small spark to ignite a child's mind. Neural Networks for Babies by Chris Ferrie is a colorfully simple introduction to the study of how machines and computing systems are created in a way that was inspired by the biological neural networks in animal and human brains. With scientific and mathematical information from an expert, this installment of the Baby University board book series is the perfect book for enlightening the next generation of geniuses. After all, it's never too early to become a scientist! If you're looking for programming for babies, coding for babies, or more Baby University board books to surprise your little one, look no further! Neural Networks for Babies offers fun early learning for your little scientist!

Society of Illustrators, Dilys Evans Founder's Award Winner A New York Times Best Book of 2020 A Wall Street Journal Best Book of 2020 PRAISE "Electrifying. Extraordinary. Enigmatic and gorgeous." —The Wall Street Journal "An epic dream captured in superbly meticulous detail." —Shaun Tan "Danger, magic, surprise and awe abound in this masterly, wordless debut." —The New York Times "I love Van den Ende's passion." —Brian Selznick, New York Times Book Review STARRED REVIEWS ★ "Marvelously engrossing—a triumph." —Kirkus Reviews, starred review ★ "Remarkable. Absolutely sui generis." —Booklist, starred review Without a word, The Wanderer presents one little paper boat's journey across the ocean, past reefs and between icebergs, through schools of fish, swaying water plants, and terrifying sea monsters. The little boat is all alone, and while its aloneness gives it the chance to wonder at the fairy-tale world above and below the waves, that also means it must save itself when it storms. And so it does. Readers young and old will find the strength and inspiration in this quietly powerful story about growing, learning, and life's ups and downs.

Eric the ladybug is an artist and traveler. He went to a mountain to watch the sunset and drew a painting of it. The next day when he woke up, he feels so thirsty and needs to find some water to drink. Will he be able to find the lowest point near him in order to find a water source? After an adventure with Eric the thirsty ladybug, you will know the most important intuition in machine learning, gradient descent.

An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.

Learn to expertly apply a range of machine learning methods to real data with this practical guide. Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math. As you work through the book, you’ll learn how to implement a range of powerful ML techniques, starting with the k-Nearest Neighbors (k-NN) method and random forests, and moving on to gradient boosting, support vector machines (SVMs), neural networks, and more. With the aid of real datasets, you’ll delve into regression models through the use of a bike-sharing dataset, explore decision trees by leveraging New York City taxi data, and dissect parametric methods with baseball player stats. You’ll also find expert tips for avoiding common problems, like handling “dirty” or unbalanced data, and how to troubleshoot pitfalls. You’ll also explore: How to deal with large datasets and techniques for dimension reduction Details on how the Bias-Variance Trade-off plays out in specific ML methods Models based on linear relationships, including ridge and LASSO regression Real-world image and text classification and how to handle time series data Machine learning is an art that requires careful tuning and tweaking. With The Art of Machine Learning as your guide, you’ll master the underlying principles of ML that will empower you to effectively use these models, rather than simply provide a few stock actions with limited practical use. Requirements: A basic understanding of graphs and charts and familiarity with the R programming language

This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students.Related Link(s)

Machine learning in artificial intelligence is finally accessible to kids! Mike the squirrel is digging peanuts to give as a present for his friend's birthday. Since peanuts grow underground, Mike needs to predict how deep he should dig. After a trip with Mike, you and your loved ones will know what linear regression is before you realize it!

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.