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[center]![[Obrazek: 47d0f40b1822cb464783ab107178f74e.jpg]](https://i126.fastpic.org/big/2025/1222/4e/47d0f40b1822cb464783ab107178f74e.jpg)
English | 2025 | ISBN: 9781633434547 | 205 pages | True PDF,EPUB | 25.28 MB[/center]
Machine Learning (ML) is a broad term for software that can spot patterns in data and make decisions without being explicitly programmed for each task. ML algorithms power the search and recommendation systems, business workflows, and software security systems you use every day-including AI tools like ChatGPT. This unique book brings the core ideas of ML to life with vivid examples, engaging exercises, and crisp illustrations. There's no jargon or complex academic theory. All you need is basic programming knowledge, high school mathematics, and curiosity!
Grokking Machine Learning, Second Edition helps you build an intuitive understanding of machine learning from the ground up. Each chapter introduces a core ML concept, such as regression and tree-based methods, data preprocessing, feature engineering, neural networks, and more. This totally-revised second edition also illuminates modern AI, including transformers, LLMs, and image generation models. You'll especially appreciate the easy-to-follow Python-based exercises and hands-on mini-projects that encourage you to practice as you learn.
English | 2025 | ISBN: 9781633434547 | 205 pages | True PDF,EPUB | 25.28 MB[/center]
Machine Learning (ML) is a broad term for software that can spot patterns in data and make decisions without being explicitly programmed for each task. ML algorithms power the search and recommendation systems, business workflows, and software security systems you use every day-including AI tools like ChatGPT. This unique book brings the core ideas of ML to life with vivid examples, engaging exercises, and crisp illustrations. There's no jargon or complex academic theory. All you need is basic programming knowledge, high school mathematics, and curiosity!
Grokking Machine Learning, Second Edition helps you build an intuitive understanding of machine learning from the ground up. Each chapter introduces a core ML concept, such as regression and tree-based methods, data preprocessing, feature engineering, neural networks, and more. This totally-revised second edition also illuminates modern AI, including transformers, LLMs, and image generation models. You'll especially appreciate the easy-to-follow Python-based exercises and hands-on mini-projects that encourage you to practice as you learn.
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