Books › Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
856 pages
A pragmatic, roll-up-your-sleeves guide that transforms intimidating AI concepts into accessible, executable code.
What it feels like
Reading this book feels like sitting down with a brilliant, patient senior engineer who genuinely wants you to succeed. The pace is steady and deliberate, guiding you through the dense thicket of machine learning with a reassuringly practical focus. Instead of drowning you in abstract mathematics, it grounds every concept in executable code, creating a mood of focused empowerment. The prose is conversational yet precise, breaking down intimidating neural networks and algorithms into intuitive, bite-sized lessons. As you progress from simple regressions to complex deep learning models, the initial sense of overwhelm gives way to the deep satisfaction of building things that actually work. It is an essential, intellectually stimulating companion for pragmatic coders ready to demystify artificial intelligence.
- Mood
- focused
- intellectually stimulating
- empowering
- Tone
- pragmatic
- encouraging
- authoritative
- accessible
- Pace
- steady
- Themes
- demystifying complexity · practical application · continuous learning · building intuition
- The writing
- Clear, conversational, and highly structured, seamlessly weaving intuitive explanations with executable code.
- The arc
- Moves from the initial intimidation of complex algorithms to a satisfying, hard-won sense of practical mastery.
- Who it's for
- Pragmatic software engineers and aspiring data scientists who learn best by doing rather than reading pure theory.
About the book
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow 2--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
NEW FOR THE SECOND EDITION: Updated all code to TensorFlow 2Introduced the high-level Keras APINew and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more.
Description from Open Library.
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