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 -- Support files, eBooks, discount offers, and more -- 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.