Title Page Copyright and Credits Training Systems Using Python Statistical Modeling About Packt Why subscribe? Packt.com Contributors About the author Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Reviews Classical Statistical Analysis Technical requirements Computing descriptive statistics Preprocessing the data Computing basic statistics Classical inference for proportions Computing confidence intervals for proportions Hypothesis testing for proportions Testing for common proportions Classical inference for means Computing confidence intervals for means Hypothesis testing for means Testing with two samples One-way analysis of variance (ANOVA) Diving into Bayesian analysis How Bayesian analysis works Using Bayesian analysis to solve a hit-and-run Bayesian analysis for proportions Conjugate priors for proportions Credible intervals for proportions Bayesian hypothesis testing for proportions Comparing two proportions Bayesian analysis for means Credible intervals for means Bayesian hypothesis testing for means Testing with two samples Finding correlations Testing for correlation Summary Introduction to Supervised Learning Principles of machine learning Checking the variables using the iris dataset The goal of supervised learning Training models Issues in training supervised learning models Splitting data Cross-validation Evaluating models Accuracy Precision Recall F1 score Classification report Bayes factor Summary Binary Prediction Models K-nearest neighbors classifier Training a kNN classifier Hyperparameters in kNN classifiers Decision trees Fitting the decision tree Visualizing the tree Restricting tree depth Random forests Optimizing hyperparameters Naive Bayes classifier Preprocessing the data Training the classifier Support vector machines Training a SVM Logistic regression Fitting a logit model Extending beyond binary classifiers Multiple outcomes for decision trees Multiple outcomes for random forests Multiple outcomes for Naive Bayes One-versus-all and one-versus-one classification Summary Regression Analysis and How to Use It Linear models Fitting a linear model with OLS Performing cross-validation Evaluating linear models Using AIC to pick models Bayesian linear models Choosing a polynomial Performing Bayesian regression Ridge regression Finding the right alpha value LASSO regression Spline interpolation Using SciPy for interpolation 2D interpolation Summary Neural Networks An introduction to perceptrons Neural networks The structure of a neural network Types of neural networks The MLP model MLPs for classification Optimization techniques Training the network Fitting an MLP to the iris dataset Fitting an MLP to the digits dataset MLP for regression Summary Clustering Techniques Introduction to clustering Computing distances Exploring the k-means algorithm Clustering the iris dataset Compressing images with k-means Evaluating clusters The elbow method The silhouette method Hierarchical clustering Clustering the iris dataset Clustering the Headlines dataset Spectral clustering Clustering the Headlines dataset Summary Dimensionality Reduction Introducing dimensionality reduction Uses of dimensionality reduction Principal component analysis Demonstration of PCA Choosing the number of components Singular value decomposition SVD for image compression Low-rank approximation Reconstructing the image using compact SVD Low-dimensional representation Example of MDS MDS in action How MDS comes into the picture Constructing distances Summary Other Books You May Enjoy Leave a review - let other readers know what you think