Designing Machine Learning Systems with Python > PACKT 원서리스트

1: Thinking in Machine Learning
The human interface
Design principles
Summary

2: Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Matplotlib
Pandas
SciPy
Scikit-learn
Summary

3: Turning Data into Information
What is data?
Big data
Signals
Cleaning data
Visualizing data
Summary

4: Models – Learning from Information
Logical models
Tree models
Rule models
Summary

5: Linear Models
Introducing least squares
Logistic regression
Multiclass classification
Regularization
Summary

6: Neural Networks
Getting started with neural networks
Logistic units
Cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Summary

7: Features – How Algorithms See the World
Feature types
Operations and statistics
Structured features
Transforming features
Principle component analysis
Summary

8: Learning with Ensembles
Ensemble types
Bagging
Boosting
Ensemble strategies
Summary

9: Design Strategies and Case Studies
Evaluating model performance
Model selection
Learning curves
Real-world case studies
Machine learning at a glance
Summary

Appendix A: Index