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

• Chapter 1: Let's Discuss Learning
• Chapter 2: Some Technical Background
• Chapter 3: Predicting Categories: Getting Started with Classification
• Chapter 4: Predicting Numerical Values: Getting Started with Regression
• Part II: Evaluation
• Chapter 5: Evaluating and Comparing Learners
• Chapter 6: Evaluating Classifiers
• Chapter 7: Evaluating Regressors
• Part III: More Methods and Fundamentals
• Chapter 8: More Classification Methods
• Chapter 9: More Regression Methods
• Chapter 10: Manual Feature Engineering: Manipulating Data for Fun and Profit
• Chapter 11: Tuning Hyperparameters and Pipelines
• Part IV: Adding Complexity
• Chapter 12: Combining Learners
• Chapter 13: Models That Engineer Features for Us
• Chapter 14: Feature Engineering for Domains: Domain-Specific Learning
• Chapter 15: Connections, Extensions, and Further Directions