Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
How Machines Learn (Simplified AI Concepts): A Simple Guide to Big Ideas

How Machines Learn (Simplified AI Concepts): A Simple Guide to Big Ideas

Martian, Nova

Paperback

General Computers

ISBN13: 9798286045976
Publisher: Independently Published
Published: May 31 2025
Pages: 254
Weight: 0.56
Height: 0.53 Width: 5.00 Depth: 8.00
Language: English
In How Machines Learn (Simplified AI Concepts): A Simple Guide to Big Ideas, readers embark on an accessible journey through the fundamentals of artificial intelligence and machine learning. The book begins by demystifying the nature of intelligence, mapping its evolution from human cognition to the algorithms powering modern technology. Through engaging historical context and real-world examples, it dismantles common myths, clarifies essential concepts, and explores how data and algorithms work in tandem to bring AI into our everyday lives.

The guide methodically unpacks the building blocks of machine learning, covering supervised, unsupervised, and reinforcement learning in clear, understandable language. Each type is illustrated with relatable scenarios, such as spam detection and anomaly discovery, while illuminating core ideas like training, features, and dealing with common challenges such as bias or overfitting. Further, it introduces neural networks and deep learning, explaining both the transformative impact and the limitations of these technologies, as well as practical techniques for preparing data, evaluating models, and ensuring trustworthiness through explainable AI.

Concluding with a thoughtful exploration of ethics, societal impact, and the future of AI, the book emphasizes responsible innovation and the enduring role of human judgment. It examines not only the opportunities brought by AI but also the critical questions around fairness, privacy, and accountability. Balancing technical clarity with big-picture insights, How Machines Learn is an ideal starting point for students, professionals, and enthusiasts eager to understand and thoughtfully navigate our increasingly AI-driven world.

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