Содержание

• Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: 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 worksUsing 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; Chapter 2: Introduction to Supervised Learning; Principles of machine learning; Checking the variables using the iris dataset; The goal of supervised learning
• Training modelsIssues in training supervised learning models; Splitting data; Cross-validation; Evaluating models; Accuracy; Precision; Recall; F1 score; Classification report; Bayes factor; Summary; Chapter 3: 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 modelExtending 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; Chapter 4: 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 interpolation2D interpolation; Summary; Chapter 5: 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; Chapter 6: 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