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by Gavin Hackeling
Mastering Machine Learning with scikit-learn
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Mastering Machine Learning with scikit-learn
Table of Contents
Mastering Machine Learning with scikit-learn
Credits
About the Author
About the Reviewers
www.PacktPub.com
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Why subscribe?
Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
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 scikit-learn on Windows
Installing scikit-learn on Linux
Installing scikit-learn on OS X
Verifying the installation
Installing pandas and matplotlib
Summary
2. Linear Regression
Simple linear regression
Evaluating the fitness of a model with a cost function
Solving ordinary least squares for simple linear regression
Evaluating the model
Multiple linear regression
Polynomial regression
Regularization
Applying linear regression
Exploring the data
Fitting and evaluating the model
Fitting models with gradient descent
Summary
3. Feature Extraction and Preprocessing
Extracting features from categorical variables
Extracting features from text
The bag-of-words representation
Stop-word filtering
Stemming and lemmatization
Extending bag-of-words with TF-IDF weights
Space-efficient feature vectorizing with the hashing trick
Extracting features from images
Extracting features from pixel intensities
Extracting points of interest as features
SIFT and SURF
Data standardization
Summary
4. From Linear Regression to Logistic Regression
Binary classification with logistic regression
Spam filtering
Binary classification performance metrics
Accuracy
Precision and recall
Calculating the F1 measure
ROC AUC
Tuning models with grid search
Multi-class classification
Multi-class classification performance metrics
Multi-label classification and problem transformation
Multi-label classification performance metrics
Summary
5. Nonlinear Classification and Regression with Decision Trees
Decision trees
Training decision trees
Selecting the questions
Information gain
Gini impurity
Decision trees with scikit-learn
Tree ensembles
The advantages and disadvantages of decision trees
Summary
6. Clustering with K-Means
Clustering with the K-Means algorithm
Local optima
The elbow method
Evaluating clusters
Image quantization
Clustering to learn features
Summary
7. Dimensionality Reduction with PCA
An overview of PCA
Performing Principal Component Analysis
Variance, Covariance, and Covariance Matrices
Eigenvectors and eigenvalues
Dimensionality reduction with Principal Component Analysis
Using PCA to visualize high-dimensional data
Face recognition with PCA
Summary
8. The Perceptron
Activation functions
The perceptron learning algorithm
Binary classification with the perceptron
Document 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
Classifying handwritten digits
Classifying characters in natural images
Summary
10. From the Perceptron to Artificial Neural Networks
Nonlinear decision boundaries
Feedforward and feedback artificial neural networks
Multilayer perceptrons
Minimizing the cost function
Forward propagation
Backpropagation
Approximating XOR with Multilayer perceptrons
Classifying handwritten digits
Summary
Index
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