Book Review: Machine Learning for Kids – insideBIGDATA

Book Review: Machine Learning for Kids – insideBIGDATA

I greatly enjoyed reading and reviewing this delightful new book, Machine Learning for Kids: A Project-based Introduction to Artificial Intelligence, by Dale Lane, which was developed to introduce machine learning technology to children. It is well-written and includes everything needed to jump-start a kid’s life in data science. The book is just the thing to motivate a young person to extend their innate curiosity to data centric experimentation. Included are the solutions to many contemporary problems where the reader turns machine learning models into computer games and apps such as a Rock, Paper Scissors game that recognizes hand shapes, an interactive virtual assistant like Siri and Alexa, a movie recommendation app, and an AI motivated Pac Man app.

Kids learn about the building blocks of programming for machine learning using the Scratch-based companion website, implementing an extension to Scratch that allows kids to train their own ML models and use them from their Scratch programs. Scratch is an easy-to-learn “low-code, no-code” visual programming environment designed to teach programming concepts without actually write lines of code. I first heard of the Scratch visual programming language while watching an episode of Shark Tank when entrepreneur Samantha John was pitching her Hopscotch app which uses a programming tool motivated by Scratch.

Machine Learning for Kids started as a coding tool that Dale created for local schools near his home in the UK and now has turned into one of the most used IBM Activity Kits ever. In 2018, Dale was awarded the IBM Volunteer Excellence Award—the highest form of volunteer recognition given by the company.

A Scratch project template for playing Rock, Paper, Scissors

Here is a list of the chapters in the book. Additionally, there is a wonderful Forward written by industry luminary Grady Booch, IBM Fellow and Chief Scientist for Software Engineering, IBM Research (whose book on OO analysis and design is an absolute classic, and whose Tweets from the shores of Maui brighten my days).

Chapter 1. What is Artificial Intelligence?
Chapter 2. Introducing Machine Learning for Kids
Chapter 3. Sorting Animal Pictures
Chapter 4. Playing Rock, Paper, Scissors Against Your Computer
Chapter 5. Recognizing Movie Posters
Chapter 6. Mail Sorting
Chapter 7. Insulting a Computer
Chapter 8. Recognizing Language in Newspapers
Chapter 9. Finding an Object in a Picture
Chapter 10. Smart Assistants
Chapter 11. Chatbots
Chapter 12. Avoiding the Monster
Chapter 13. Tic Tac Toe
Chapter 14. Confusing the Computer
Chapter 15. Biasing the Computer
Chapter 16. Afterword

This is another excellent title from technology publisher No Starch Press. Over the past few years, I’ve been impressed with the breadth and timeliness of the books coming from No Starch. All their books occupy a big section of my personal library. Machine Learning for Kids is another leading-edge resource for data scientists, this time for very young data scientists. I believe the sweet-spot for the book is ages 9-13. At 14 and beyond is when kids can start using traditional languages for data science like R and Python (I started my programming career at this age).

Each chapter contains a brief introduction to each project followed by sections outlined below. The bulk of the book consists of detailed instructions for how to construct each project using Scratch:

  • Build Your Project: train your model, prepare your project, test your project
  • Review and Improve Your Project: measuring performance accuracy with a confusion matrix, and precision and recall
  • What You Learned

Author Dale Lane is an award-winning, UK-based IBM developer who has worked on IBM’s groundbreaking AI Watson platform since 2011. Here is a video presentation by Lane that highlights many of the concepts found in his book. The presentation encapsulates the work he’s done to introduce machine learning to kids, and demo the resources that are available to give kids hands-on experiences at training and using machine learning models for themselves.

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I already alerted my Introduction to Data Science students at UCLA that they’re getting some competition from kids being brought up with machine learning using Scratch. They better get moving, or they may be passed along the way!

Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist for insideBIGDATA. In addition to being a tech journalist, Daniel also is a consultant in data scientist, author, educator and sits on a number of advisory boards for various start-up companies. 

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