Artificial intelligence is reshaping work and daily life, yet most explanations are either too technical or too vague. This book walks beginners through what AI really is, how machine learning and generative AI work, and where they succeed and fail. With diagrams, short code examples, case studies and exercises, you will learn to use AI tools effectively, judge their output critically and take part confidently in conversations about AI's risks and future.
Level: beginner · 11 chapters · about 6 h · Free · AI-assisted ebook created by Sandip Das
What you will learn
Explain in plain language what AI, machine learning, deep learning and generative AI are and how they relate
Describe how a machine learning model learns from data, from training to prediction
Use generative AI tools such as chatbots effectively with well-structured prompts
Evaluate AI output critically, recognising hallucinations, bias and other common failure modes
Identify realistic opportunities to apply AI in work and everyday life, and the limits of what it can do
Discuss the ethical, privacy, safety and societal questions raised by AI
Run and modify a simple machine learning example in Python
Contents
Part 1. Part I: How AI Works
Builds a clear mental model of AI from scratch: what it is, where it came from, how machines learn from data, the main kinds of learning, neural networks, and a first hands-on model in Python.
What AI Is and Where It Came From(30 min)· free preview Defines artificial intelligence in plain language, shows how AI, machine learning, deep learning and generative AI nest inside one another, and traces the field's history from early neural network ideas and the Turing Test through the 1956 Dartmouth workshop to today's industry-led boom. Includes a diagram of the nested concepts and a short glossary of first terms.
How Machines Learn: Data, Models, Training and Prediction(35 min) Walks through the core loop of machine learning using everyday examples: datasets made of examples with features and labels, a model as a set of learned numbers, training versus inference, splitting data into training, validation and test sets, overfitting versus generalisation, and simple ways to judge a model with accuracy, precision and recall.
The Main Kinds of Machine Learning(30 min) Introduces the three classic paradigms of machine learning — supervised, unsupervised and reinforcement learning — plus self-supervised learning, which powers modern language models. Uses worked examples such as spam filtering, customer grouping with clustering and learning by reward, with a comparison diagram and exercises for matching problems to approaches.
Neural Networks and Deep Learning, Intuitively(35 min) Explains neural networks without mathematics: layers of simple units, learning by gradually adjusting weights through backpropagation, why depth helps, and how convolutional networks recognise images. Tells the story of ImageNet and AlexNet that launched the deep learning boom, and looks at the growing scale and energy cost of modern models.
Hands-On: Your First Machine Learning Model in Python(40 min) A gentle, optional hands-on chapter for complete beginners. Readers open a free browser notebook, load a small dataset, train a scikit-learn classifier with fit and predict, measure it on held-out data, swap in a different model, try clustering, and learn to avoid common pitfalls such as data leakage by using a pipeline. Each step is explained so non-coders can follow along.
Part 2. Part II: Generative AI in Practice
Explains how large language models and chatbots work, how to use them well with clear prompts, and how to judge their output critically.
Generative AI and Large Language Models(35 min) Explains what happens inside a chatbot: text split into tokens, tokens turned into embeddings, a transformer predicting the next token, pretraining on vast amounts of text and fine-tuning with human feedback (RLHF) to make models helpful. Also shows how fast models change and are retired, so readers learn concepts rather than product details.
Using AI Assistants Well: Prompting and Everyday Workflows(35 min) A practical guide to getting good results from chatbots such as ChatGPT: structuring prompts with clear instructions, context, format and examples; iterating on answers; using custom instructions and projects for ongoing work; and managing privacy settings and data controls. Includes before-and-after prompt examples and exercises.
Thinking Critically About AI Output: Hallucination, Bias and Other Failures(35 min) Explains why language models produce confident falsehoods, how bias enters AI systems through data, design and social context, how prompt injection manipulates AI tools, and why poor data quality undermines results. Uses real cases such as a chatbot giving wrong refund advice and offers a practical checklist for verifying AI output.
Part 3. Part III: AI in Work and Society
Looks at where AI delivers real value, the ethical and regulatory questions it raises, and how it is changing jobs and how readers can prepare.
AI in Business and Everyday Life: What Really Works(35 min) Surveys how organisations and individuals actually use AI, separating hype from evidence. Case studies include a customer-support assistant that most helped novice workers, a controlled study in which experienced developers were slower with AI despite feeling faster, and a chatbot whose wrong advice made its company liable. Ends with a framework for spotting good AI opportunities.
Ethics, Privacy, Safety and Regulation(35 min) Introduces the main ethical questions around AI — fairness, transparency, accountability, privacy and safety — and the frameworks that address them, including the OECD AI Principles, the NIST AI Risk Management Framework and the staged rollout of the EU AI Act. Uses rising incident data to show why responsible AI matters for individuals and organisations.
AI, Jobs and Preparing for the Future(30 min) Examines what the evidence says about AI's effect on jobs: which occupations are most exposed, forecasts of jobs created and displaced, and differences between countries. Closes the book with practical steps for building AI literacy and skills, a recap of key ideas and a consolidated glossary.