Mark Fenner: Machine Learning with Python for Everyone
Machine Learning with Python for Everyone
Buch
- Pearson Education (US), 12/2019
- Einband: Kartoniert / Broschiert
- ISBN-13: 9780134845623
- Bestellnummer: 8236235
- Umfang: 592 Seiten
- Gewicht: 912 g
- Maße: 228 x 179 mm
- Stärke: 31 mm
- Erscheinungstermin: 10.12.2019
Klappentext
Students are rushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine Learning with Python for Everyone brings together all they'll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently.Reflecting 20 years of experience teaching non-specialists, Dr. Mark Fenner teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, Fenner presents real-world case studies that apply his lessons in detailed, nuanced ways. Throughout, he offers clear narratives, practical "code-alongs,” and easy-to-understand images -- focusing on mathematics only where it's necessary to make connections and deepen insight.
All students need to succeed in data science with Python: process, code, and implementation
Students will understand the machine learning process, leverage the powerful Python scikit-learn library, and master the algorithmic components of learning systems
Integrates clear narrative, carefully designed Python code, images, and interesting, intelligible datasets
All you need to succeed in data science with Python: process, code, and implementation
Understand the machine learning process, leverage the powerful Python scikit-learn library, and master the algorithmic components of learning systems
Integrates clear narrative, carefully designed Python code, images, and interesting, intelligible datasets
For wide audiences of analysts, managers, project leads, statisticians, developers, and students who want a quick jumpstart into data science