This book will acquaint you with various aspects of statistical analysis in Python. You will work with different types of prediction models, such as decision trees, random forests and neural networks. By the end of this book, you will be confident in using various Python packages to train your own models for effective machine learning.

• 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 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
• Chapter 2: 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
• 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 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
• 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 interpolation
    • 2D 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
    • The silhouette method
  • Hierarchical clustering
    • Clustering the iris dataset
    • Clustering the Headlines dataset
  • Spectral clustering
    • Clustering the Headlines dataset
  • Summary
• Chapter 7: 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
• Index