Luther-Bibel

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. 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