Mastering Machine Learning with scikit-learn > PACKT 원서리스트

1: The Fundamentals of Machine Learning
Learning from experience
Machine learning tasks
Training data and test data
Performance measures, bias, and variance
An introduction to scikit-learn
Installing scikit-learn
Installing pandas and matplotlib
Summary

2: Linear Regression
Simple linear regression
Evaluating the model
Multiple linear regression
Polynomial regression
Regularization
Applying linear regression
Fitting models with gradient descent
Summary

3: Feature Extraction and Preprocessing
Extracting features from categorical variables
Extracting features from text
Extracting features from images
Data standardization
Summary

4: From Linear Regression to Logistic Regression
Binary classification with logistic regression
Spam filtering
Binary classification performance metrics
Calculating the F1 measure
ROC AUC
Tuning models with grid search
Multi-class classification
Multi-label classification and problem transformation
Summary

5: Nonlinear Classification and Regression with Decision Trees
Decision trees
Training decision trees
Decision trees with scikit-learn
Summary

6: Clustering with K-Means
Clustering with the K-Means algorithm
Evaluating clusters
Image quantization
Clustering to learn features
Summary

7: Dimensionality Reduction with PCA
An overview of PCA
Performing Principal Component Analysis
Using PCA to visualize high-dimensional data
Face recognition with PCA
Summary

8: The Perceptron
Activation functions
Binary classification with the perceptron
Limitations of the perceptron
Summary

9: From the Perceptron to Support Vector Machines
Kernels and the kernel trick
Maximum margin classification and support vectors
Classifying characters in scikit-learn
Summary

10: From the Perceptron to Artificial Neural Networks
Nonlinear decision boundaries
Feedforward and feedback artificial neural networks
Approximating XOR with Multilayer perceptrons
Classifying handwritten digits
Summary

Appendix A: Index