Ai Me Big Idea 3 Machine Learning

Download or read book AI+Me: Big Idea 1 - Perception written by ReadyAI and published by Independently Published. This book was released on 2020-06-23 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: Is your child interested in sci-fi, robots, or video games? Is your kid fascinated by smart home assistants and the prospect of self-driving cars? Time to turn that enthusiasm into action and engage with the exciting world of artificial intelligence! AI+Me is a series designed to introduce the 5 Big Ideas of Artificial Intelligence to young learners. Students take a deep dive into the Five Big Ideas of AI (Perception, Representation and Reasoning, Learning, Natural Interaction, and Societal Impact). This is the 1st book in the AI+Me series focused on Perception. The series is recommended for K-2 students. Why should children be educated about AI? Learning AI opens up a world of opportunities. As the fastest growing area of computer science, AI will become the most important change force when our children grow up so it is critical they learn about it early. AI is fun! The field of AI started with scientists making computers learn to play games. AI is an incredibly fun way to introduce kids to programming and pique their interest in advanced topics like deep learning. Lastly, a topic like AI naturally opens up discussions about our humanity. In our curriculum, we dig deep into questions like "does AI positively or negatively impact society?" In doing so we aim to develop critical thinking skills and encourage students to reflect deeply. Benefits of AI education Gets children interested in #STEM education Improves their problem-solving and critical-thinking skills Builds their understanding of the tech tools that'll shape their future Starts important conversations about the future of humanity What are parents saying? "My 1st grader loves this book. She already is really interested in computers, but this book got her thinking about how we actually tell emotions. She started using her camera on her computer to record different expressions." "My son learned ReadyAI courses before. I let his friend read AI+Me big idea 1. Surprisingly, both of them finished reading the book, with a lot of interest! I Will recommend this book for elementary school students." "I have been looking for fun ways to introduce AI to my kid, and this definitely nailed it."

Download or read book Artificial Intelligence & Me (Special Edition) written by Readyai and published by . This book was released on 2020-11-23 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Artificial Intelligence & Me' is a book that introduces & explains the 5 Big Ideas in AI to kids. It does so with the help of stories, activities, and engaging puzzles.

Download or read book Machine Learning: How Artificial Intelligence Learns (Fun Picture Book for K-2, AI+ME Series, Big Idea 3) written by ReadyAI and published by Ready AI LLC. This book was released on 2020-10-19 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: Is your child interested in sci-fi, robots, or video games? Is your kid fascinated by smart home assistants and the prospect of self-driving cars? Time to turn that enthusiasm into action and engage with the exciting world of artificial intelligence! AI+Me is a series designed to introduce the 5 Big Ideas of Artificial Intelligence to young learners. Students take a deep dive into the Five Big Ideas of AI (Perception, Representation and Reasoning, Learning, Natural Interaction, and Societal Impact). This is the 3rd book in the AI+Me series focused on Learning. The series is recommended for K-2 students. Why should children be educated about AI? Learning AI opens up a world of opportunities. As the fastest growing area of computer science, AI will become the most important change force when our children grow up so it is critical they learn about it early. AI is fun! The field of AI started with scientists making computers learn to play games. AI is an incredibly fun way to introduce kids to programming and pique their interest in advanced topics like deep learning. Lastly, a topic like AI naturally opens up discussions about our humanity. In our curriculum, we dig deep into questions like “does AI positively or negatively impact society?” In doing so we aim to develop critical thinking skills and encourage students to reflect deeply. Benefits of AI education: - Gets children interested in #STEM education - Improves their problem-solving and critical-thinking skills - Builds their understanding of the tech tools that’ll shape their future - Starts important conversations about the future of humanity What are educators saying: “I really love these books. I think they are absolutely beautiful and very visually engaging ways for students to learn about artificial intelligence. I like how they progress through the topic and terms related to artificial intelligence and help students to attach meaning to what they are learning by the different examples and step-by-step ways that students build their understanding through the book.” - Rachelle Dene Poth, Author of In Other Words, Unconventional, The Future is Now, and Chart a New Course. What are parents saying: “My 1st grader loves this book. She already is really interested in computers, but this book got her thinking about how we actually tell emotions. She started using her camera on her computer to record different expressions.” “My son learned ReadyAI courses before. I let his friend read AI+Me big idea 1. Surprisingly, both of them finished reading the book, with a lot of interest! I Will recommend this book for elementary school students.” “I have been looking for fun ways to introduce AI to my kid, and this definitely nailed it.”

Download or read book Machine Learning in Action written by Peter Harrington and published by Simon and Schuster. This book was released on 2012-04-03 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2016-10-07 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as “Big Data” has gotten bigger, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of “data science,” and discusses the ethical and legal implications for data privacy and security.

Download or read book Human Frontiers written by Michael Bhaskar and published by MIT Press. This book was released on 2022-08-02 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why has the flow of big, world-changing ideas slowed down? A provocative look at what happens next at the frontiers of human knowledge. The history of humanity is the history of big ideas that expand our frontiers—from the wheel to space flight, cave painting to the massively multiplayer game, monotheistic religion to quantum theory. And yet for the past few decades, apart from a rush of new gadgets and the explosion of digital technology, world-changing ideas have been harder to come by. Since the 1970s, big ideas have happened incrementally—recycled, focused in narrow bands of innovation. In this provocative book, Michael Bhaskar looks at why the flow of big, world-changing ideas has slowed, and what this means for the future. Bhaskar argues that the challenge at the frontiers of knowledge has arisen not because we are unimaginative and bad at realizing big ideas but because we have already pushed so far. If we compare the world of our great-great-great-grandparents to ours today, we can see how a series of transformative ideas revolutionized almost everything in just a century and a half. But recently, because of short-termism, risk aversion, and fractious decision making, we have built a cautious, unimaginative world. Bhaskar shows how we can start to expand the frontier again by thinking big—embarking on the next Universal Declaration of Human Rights or Apollo mission—and embracing change.