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 |