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Product 1
Call to Play: Data to AI, An Exercise Book on Algorithms
A 304-page, full-colour workbook teaching AI/ML algorithms from foundational concepts to LLMs. Learners read a story to understand the concept, work through pen-and-paper exercises, then code it. The book is designed for anyone aged 15+ with a unique, attention-friendly design. The book is student-friendly, priced at Rs. 649.
Sector
Education
Industry
EdTech
Model
B2B
D2C
Technology
AI / ML
Learn algorithms through Indian stories, hand-drawn data sketches, and code.
Call to Play: Data to AI is an AI/ML algorithm book built on real-world experiential learning. It covers 28 topics across four modules: Foundations, Core ML Algorithms, Neural Networks, and Generative AI. Each uses a consistent pedagogy of experiential learning.
What makes this book different:
Each chapter opens with a short story set in an Indian neighbourhood: a boy learning to shield his candle from a monsoon wind, two cousins training their stray pups in opposite ways, and a college gossip whose talent for connecting dots mirrors how transformers process language.
These stories are not decoration; they are the first layer of understanding, designed so the concept seeps in before the technical explanation begins. Each chapter follows the same arc. First, the story. Then the science: clear explanations of how the algorithm works. Then a data sketching activity inspired by data artist Stefanie Posavec, where you collect data from your own life and draw patterns by hand. These analogue activities train your brain to think about algorithms in relation to your lived experience. Then flowcharts to colour, mind maps to complete, and Google Colab prompts to generate working code, run it, and play with it.
Every chapter gives you two Kaggle project ideas to build and share on GitHub. By the time you finish the book, you will have a portfolio of up to 54 projects. Each one is proof of what you can do, not just what you know. The projects grow in complexity as you move through the book. For a potential employer, your portfolio tells the story of someone who started from zero and kept going.
Each chapter closes with a "Teach What You Learned" activity. These are not summaries. They ask you to explain what you learned through film editing, collages, meme cards, or comedy sketches. Teaching in unexpected formats forces you to find the gaps in your own understanding.
What you will learn:
- Module 1 - Foundations: What is Machine Learning, A Brief History of AI, Data Collection, Data Preprocessing, Supervised and Unsupervised Learning, Data Splits, Overfitting and Underfitting, Model Evaluation
- Module 2 - Core ML Algorithms: The most commonly used supervised and unsupervised learning algorithms
- Module 3 - Neural Networks: Perceptron, Multilayer Perceptron, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory
- Module 4 - Generative AI and Learning Paradigms: Variational Autoencoders, Reinforcement Learning, Generative Adversarial Networks, Diffusion Models, Transformers, Large Language Models
Who this is for:
- Beginners: You want to understand AI/ML algorithms from the very beginning, without any prior knowledge beyond basic Python.
- Working professionals: You became familiar with ML once but set it aside. This time, you will not be memorising. You will be building intuition through experiential learning.
- Educators: You want a new way to teach algorithms, one that keeps students active, curious, and coming back for more.