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

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.