Are you an experienced statistician or data professional looking for a powerful, efficient, and versatile programming language to turbocharge your data analysis and machine learning projects? Look no further! "Statistics with Rust" is your comprehensive resource to unlock Rust's true potential in modern statistical methods.
This book is tailored specifically for statisticians and data professionals who are already familiar with the fundamentals of statistics and want to leverage the speed and reliability of Rust in their projects. Over 11 in-depth chapters, you will discover how Rust outperforms Python in various aspects of data analysis and machine learning and learn to implement popular statistical methods using Rust's unique features and libraries.
"Statistics with Rust" begins by introducing you to Rust's programming environment and essential libraries for data professionals. You'll then dive into data handling, preprocessing, and visualization techniques that form the backbone of any statistical analysis. As you progress through the book, you'll explore descriptive and inferential statistics, probability distributions, regression analysis, time series analysis, Bayesian statistics, multivariate statistical methods, and nonlinear models. Additionally, the book covers essential machine-learning techniques, model evaluation and validation, natural language processing, and advanced techniques in emerging topics.
To ensure you get the most out of this book, each chapter includes hands-on examples and exercises to reinforce your understanding of the concepts presented. You'll also learn to optimize your Rust code and select the best tools and libraries for each task, maximizing your productivity and efficiency.
Key Learnings
Discover Rust's unique advantages for statistical analysis and machine learning projects.
Learn to efficiently handle, preprocess, and visualize data using Rust libraries.
Implement descriptive and inferential statistics with Rust for powerful data insights.
Master probability distributions and random variables in Rust for robust simulations.
Perform advanced regression analysis with Rust's capabilities.
Explore Bayesian statistics and Markov Chain Monte Carlo methods in Rust.
Uncover multivariate techniques, including PCA and Factor Analysis, using Rust libraries.
Implement cutting-edge machine learning algorithms and model evaluation techniques in Rust.
Delve into text analysis, natural language processing, and network analysis with Rust.
Table of Content
Introduction to Rust for Statisticians
Data Handling and Preprocessing
Descriptive Statistics in Rust
Probability Distributions and Random Variables
Inferential Statistics
Regression Analysis
Bayesian Statistics
Multivariate Statistical Methods
Nonlinear Models and Machine Learning
Model Evaluation and Validation
Text and Natural Language Processing
Audience
"Statistics with Rust" is your indispensable guide to harnessing the power of Rust for modern statistical analysis and machine learning. Whether you are a seasoned data professional or a Rust enthusiast looking to expand your knowledge, this book provides the tools and insights to elevate your projects.
Author(s): Keiko Nakamura
Publisher: GitforGits
Year: 2023
Language: English
Commentary: not true
Pages: 200
Applying Simple Regression with Rust
Multiple Linear Regression
Understanding Equation
Applying Multiple Linear Regression
Polynomial Regression
Understanding Equation
Applying Polynomial Regression
Ridge and Lasso Regression
Understanding Equation
Applying Ridge and Lasso Regression
Logistic Regression
Understanding Equation
Applying Logistic Regression
Summary
Chapter 7: Bayesian Statistics
Introduction to Bayesian Statistics
Bayes Theorem
Advantages of Bayesian Statistics
Bayesian Inference
Putting Bayesian Inference into Action
Procedure to Perform Bayesian Inference
Practical Illustration of Bayesian Inference
Bayesian Model Comparison
Bayesian Hierarchical Modeling
Advanced Markov Chain Monte Carlo Method
Simple Implementation of HMC Method
Model Comparison and Selection
Model Comparison using DIC
Model Comparison using WAIC
Summary
Chapter 8: Multivariate Statistical Methods
Multivariate Statistical Methods
Introduction
Overview of Multivariate Techniques
Principal Component Analysis (PCA)
Procedure of PCA
Sample Program to Implement PCA
Canonical Correlation Analysis (CCA)
Procedure to Perform CCA
Sample Program to Implement CCA
Linear Discriminant Analysis (LDA)
Procedure to Perform LDA Algorithm
Sample Program to Implement LDA
Independent Component Analysis (ICA)
Overview of ICA Algorithm
Sample Program to Implement ICA
Multidimensional Scaling (MDS)
Types of Multidimensional Scaling
Sample Program to Implement Classical MDS
Summary
Chapter 9: Nonlinear Models and Machine Learning
Nonlinear Models
Decision Trees
Overview
Building Decision Tree
Support Vector Machines (SVM)
Overview
Building SVM Model
Neural Networks
Fundamentals of Neural Networks
Building Neural Network Model
Ensemble Methods
Overview
Building Bagging Ensemble of Decision Tree
Summary
Chapter 10: Model Evaluation and Validation
Model Evaluation and Validation
Introduction
Train-test Split Technique
Exploring Train-test Split
Implementing Train-test Split
Cross-validation Technique
Understanding Cross-validation
Implementing K-fold Cross-validation
Hyperparameter Tuning
Overview
Perform Hyperparameter Tuning using Grid Search
Model Selection Techniques: AIC and BIC
Akaike Information Criterion (AIC)
Bayesian Information Criterion (BIC)
Implement AIC and BIC
Resampling Methods
Bootstrapping
Permutation Tests
Perform Bootstrapping and Permutation Test
Implementing Bootstrapping
Implementing Permutation Test
Summary
Chapter 11: Text and Natural Language Processing
Overview of Natural Language Processing (NLP)
Key Processes of NLP
Text Preprocessing and Tokenization
Key Preprocessing Techniques
Common Tokenization Approaches
Implementing Text Preprocessing and Tokenization
Sample Program to Perform Preprocessing and Tokenization
Stopword Removal Process
Sample Program to Perform Stopword Removal
Stemming and Lemmatization
Perform Stemming
Information Retrieval with TF-IDF
TF-IDF Components
Implementation of TF-IDF
Word Embeddings and Word2Vec
Summary
Index
Epilogue
