Book 8
Essentials of Machine Learning
Contents at a Glance
- Chapter 1: Introducing How Machines Learn
- Chapter 2: Demystifying the Math behind Machine Learning
- Chapter 3: Descending the Right Curve
- Chapter 4: Validating Machine Learning
- Checking Out-of-Sample Errors
- Getting to Know the Limits of Bias
- Keeping Model Complexity in Mind
- Keeping Solutions Balanced
- Training, Validating, and Testing
- Resorting to Cross-Validation
- Looking for Alternatives in Validation
- Optimizing Cross-Validation Choices
- Avoiding Sample Bias and Leakage Traps
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