更新时间：2021-07-09 19:40:18
封面
版权信息
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Chapter 1. Thinking in Machine Learning
The human interface
Design principles
Summary
Chapter 2. Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Matplotlib
Pandas
SciPy
Scikit-learn
Chapter 3. Turning Data into Information
What is data?
Big data
Signals
Cleaning data
Visualizing data
Chapter 4. Models – Learning from Information
Logical models
Tree models
Rule models
Chapter 5. Linear Models
Introducing least squares
Logistic regression
Multiclass classification
Regularization
Chapter 6. Neural Networks
Getting started with neural networks
Logistic units
Cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Chapter 7. Features – How Algorithms See the World
Feature types
Operations and statistics
Structured features
Transforming features
Principle component analysis
Chapter 8. Learning with Ensembles
Ensemble types
Bagging
Boosting
Ensemble strategies
Chapter 9. Design Strategies and Case Studies
Evaluating model performance
Model selection
Learning curves
Real-world case studies
Machine learning at a glance
Index
