Overview

This book is for K12 students who want to learn AI, for teachers who want to teach AI and bring AI into the classroom, and for any individual who wants to understand AI in a simple and effective way. Artificial Intelligence is all around us. This book demystifies AI for K12 students and teachers using a unique combination of concept learning, hands-on plugged and unplugged exercises, context of how AI is used in industries from finance to marketing, and project ideas for students to apply their own creativity and build their own AIs. The ten fully illustrated color chapters cover both Machine Learning and Deep Learning, a comprehensive overview of AI Ethics, and popular AI algorithms from Linear Regression to Convolutional Neural Networks. Teacher's corners provide teachers with additional resources for bringing AI into the classroom. The book is paired with extensive online resources in curriculum, datasets, exercises, and code. The two authors (Nisha Talagala and Sindhu Ghanta) have extensive experience building industry AI solutions and have applied their knowledge to teach AI to K12 students. This book comes from their experiences of teaching AI to thousands of students around the world. More information can be found on www.corp.aiclub.world/ai-book-middle-school-high-school

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Reviews

Tushar Kant - Director Product Management AWS - Artificial Intelligence and Cloud Computing. Source: Amazon 'This is an amazing book that can be used by people across the spectrum (from middle school kids to industry professionals) to understand the fundamentals of Artificial Intelligence & Machine Learning. The book starts by outlining the basic concepts related to Learning in AI using Data (while highlighting Ethics & Bias related issues) and then makes the reader familiar with various core algorithms related to Classification, Regression, Nearest Neighbor & Clustering and ends with an overview of Deep Learning. What I liked about this book is that in addition to building the concepts in an easy-to-comprehend manner, it has sections dedicated in every chapter to help teachers plan their course-delivery, along with detailed coding exercises which makes this a good self-contained book for unplugged delivery. I believe that along with middle and high-school students and teachers, the L&D (Learning & Development) Departments in companies across the industry can leverage this book to educate their employees.' Balint Fleischer Source: Amazon 'Clearly written with great examples. Teachers' corners with QR code links are very useful.' Giridhar Source: Amazon 'I love how the book starts with a gentle introduction to AI coupled with hands-on activities, real life examples and discussions. It gives an amazing intuition into how AI learns and works. The deeper topics about how specific algorithms work etc are presented very well. It assumes no background and provides a path to create your own AIs. There are online activities, unplugged activities, coding exercises, teachers corners, assessments.. it has everything! Definitely recommended for middle and high school kids or even adults who want to understand this topic and build things.'

Author Information

Dr. Nisha Talagala is a world-renowned computer scientist and an expert in Artificial Intelligence and Machine Learning. The inspiration to write this book started with her experiences sharing the power of AI technology with her then 9 year old daughter. She found that there were not many resources available for kids to learn and interact with AIs in a way that is engaging and not intimidating. She found that, with the right tools and approach, kids can learn AI, become empowered, and create amazing innovations. Just like computer science and coding is an integral part of learning today, AI is required learning for all the professionals of tomorrow. AI is already here, all around our kids, and this book was created to help prepare our kids become AI Literate for their future. Nisha Talagala is the CEO and founder of AIClub.World which is bringing AI Literacy to K-12 students and individuals worldwide. Nisha has significant experience in introducing technologies like Artificial Intelligence to new learners from students to professionals. Previously, Nisha co-founded ParallelM which pioneered the MLOps practice of managing Machine Learning in production for enterprises - acquired by DataRobot. Nisha is a recognized leader in the operational machine learning space, having also driven the USENIX Operational ML Conference, the first industry/academic conference on production AI/ML. Nisha was previously a Fellow at SanDisk and Fellow/Lead Architect at Fusion-io, where she worked on innovation in non-volatile memory technologies and applications. Nisha has more than 20 years of expertise in enterprise software development, distributed systems, technical strategy and product leadership. She has worked as technology lead for server flash at Intel - where she led server platform non-volatile memory technology development, storage-memory convergence, and partnerships. Prior to Intel, Nisha was the CTO of Gear6, where she designed and built clustered computing caches for high performance I/O environments. Nisha earned her PhD at UC Berkeley where she did research on clusters and distributed systems. Nisha holds 73 patents in distributed systems and software, over 25 refereed research publications, is a frequent speaker at industry and academic events, and is a contributing writer to Forbes and other publications. Dr. Sindhu Ghanta is an expert in Artificial Intelligence and Machine Learning, with several publications and talks in world class journals and conferences. This book is an expression of her passion for teaching and making this amazing technology accessible to young minds. She is the co-founder of AIClub and works as the Head of Machine Learning. She received the Ph.D. degree in electrical and computer engineering from Northeastern University, Boston, USA, in 2014, followed by a Post-Doctoral Fellowship with BIDMC in the Department of Pathology, Harvard Medical School, where she was involved in detection and classification of features from histopathological (breast cancer) images. She worked as a research scientist with Parallel Machines on monitoring the health of machine learning algorithms in production and has many publications in the area.

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