Machine learning with python for everyone
- 1st ed.
- Chennai: Pearson, 2022.
- 473 p.: 24 cm
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, 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 &ldquocode-alongs," and easy-to-understand images focusing on mathematics only where it's necessary to make connections and deepen insight." Features 1. Covers whatever learners need to succeed in data science with Python: process, code, and implementation 2. Enables learners to understand the machine learning process, leverage the powerful Python scikit-learn library, and master the algorithmic components of learning systems 3. Integrates clear narrative, carefully designed Python code, images, and interesting, intelligible datasets Table of Content 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: Connections, Extensions, and Further Directions Salient