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Introduction to Machine Learning with Python By Andreas C. Müller
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Binding: Paperback
Language: English
ISBN-13: 978-9352134571
Reader’s Age: Adults 18+ / College Students / Tech Professionals
Ships Within: 5–10 Business Days
Author: Andreas C. Müller and Sarah Guido
Release Date: November 15, 2016
Genre: Science & Technology
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Master Machine Learning with Python – A Beginner-Friendly Guide to Data Science and AI
About the Book
“Introduction to Machine Learning with Python” is your gateway to understanding how machines learn from data. Published by O’Reilly Media, this book strips away the intimidation factor and focuses on practical implementation. You’ll learn how to build predictive models, classify data, and create intelligent systems using Python’s most powerful machine learning libraries. No PhD required—just a willingness to learn and experiment with real-world datasets.
About the Author
Andreas C. Müller is a core contributor to scikit-learn and a leading machine learning researcher. Sarah Guido is a data scientist with extensive experience making complex technical concepts accessible to learners at all levels. Together, they bring years of teaching experience and industry expertise to help you navigate the world of machine learning with confidence and clarity.
Who Is This Book For?
This book is perfect for Python developers ready to expand into data science, software engineers curious about AI applications, data analysts wanting to add predictive modeling to their toolkit, and self-taught programmers looking for a structured path into machine learning. If you have basic Python knowledge and want to understand how Netflix recommends shows or how spam filters work, this guide will take you from theory to implementation step by step.
Frequently Asked Questions (FAQs)
Q: Do I need a math background to understand machine learning with Python?
While basic math helps, this book focuses on practical implementation rather than heavy theory. You’ll learn the concepts you need as you build projects, making machine learning accessible even if calculus isn’t your strong suit.
Q: What Python libraries does this book cover for machine learning?
The book primarily teaches scikit-learn, the most popular machine learning library for Python. You’ll also work with NumPy and pandas for data manipulation, plus matplotlib for visualization—giving you a complete beginner-friendly toolkit.
Q: Is this book suitable for complete beginners in programming?
You should have basic Python programming knowledge before starting. If you understand variables, loops, and functions, you’re ready. The book teaches machine learning concepts from scratch, but assumes you’re comfortable writing simple Python code.
Q: How long does it take to learn machine learning with this book?
Most readers complete the book in 4-8 weeks with consistent study. You’ll start building simple models within the first few chapters, so you’ll see results quickly. The key is practicing with the provided code examples and experimenting with your own datasets.
Q: Can I use this book to prepare for a data science career?
Absolutely. This book provides the foundational machine learning skills that employers look for in entry-level data science and ML engineering positions. You’ll build a portfolio of projects that demonstrate practical understanding of supervised learning, unsupervised learning, and model evaluation techniques.
| Weight | 650 g |
|---|---|
| Dimensions | 18 × 1 × 24 cm |
You'll discover how to prepare data for machine learning, choose the right algorithms for different problems, build and evaluate predictive models, and avoid common pitfalls that trip up beginners. The book emphasizes hands-on learning with code examples you can run immediately, helping you gain practical skills that translate directly to real-world projects and career opportunities in the fastest-growing field in tech.
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