Machine Learning with Python for Everyone
Students are crushing 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, the author teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, the book 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 connection and deepen insight.
Table of Contents:
- Chapter 1: Let’s discuss learning
- Chapter 2: predicting categories: getting started with classification
- Chapter 3: predicting numerical values: getting started with regression
- Chapter 4: evaluating and comparing learners
- Chapter 5: evaluating classifiers
- Chapter 6: evaluating Regressors
- Chapter 7: more classification methods
- Chapter 8: more regression methods
- Chapter 9: manual feature engineering: manipulating data for fun and Profit
- Chapter 10: models that engineer features for us
- Chapter 11: feature engineering for domains: domain-specific learning online chapters
- Chapter 12: tuning hyperparameters and pipelines
- Chapter 13: combining learners
- Chapter 14: connecting, extensions, and further directions
Book | |
---|---|
Author | Fenner |
Pages | 504 |
Year | 2020 |
ISBN | 9789353944902 |
Publisher | Pearson |
Language | English |
Uncategorized | |
Subject | Computer Science / Machine Learning |
Edition | 1/e |
Weight | 760 g |
Dimensions | 24.4 x 20.3 x 3.7 cm |
Binding | Paperback |