1. Preface
  2. Introduction
  3. Rust Environment Setup
  4. 1. Introduction to Environment Setup
    1. 1.1. Installing Rust
    2. 1.2. Setting Up Rust Kernel for JupyterLab
    3. 1.3. Updating Rust
    4. 1.4. Uninstalling Rust
  5. Reading Data with Rust
  6. 2. Introduction to Data Reading
    1. 2.1. Reading Plain Text Data
    2. 2.2. Reading CSV Files
    3. 2.3. Reading Text Data
    4. 2.4. Reading Excel Data
    5. 2.5. Connecting to Databases
  7. Data Preprocessing with Rust
  8. 3. Introduction to Data Preprocessing
    1. 3.1. Data Cleaning
    2. 3.2. Handling Missing Values
    3. 3.3. Data Transformation
    4. 3.4. Feature Scaling
    5. 3.5. Encoding Categorical Variables
    6. 3.6. Splitting Data into Training and Testing Sets
  9. Data Exploration with Rust
  10. 4. Introduction to Data Exploration
    1. 4.1. Descriptive Statistics
    2. 4.2. Data Visualization
    3. 4.3. Exploratory Data Analysis (EDA)
    4. 4.4. Correlation Analysis
    5. 4.5. Pandas-like Operations with DataFrames
  11. Machine Learning with Rust
  12. 5. Introduction to Machine Learning
    1. 5.1. Supervised Learning
      1. 5.1.1. Linear Regression
      2. 5.1.2. Logistic Regression
      3. 5.1.3. Decision Trees
      4. 5.1.4. Random Forests
      5. 5.1.5. Support Vector Machines (SVMs)
    2. 5.2. Unsupervised Learning
      1. 5.2.1. Clustering (K-Means, DBSCAN)
      2. 5.2.2. Dimensionality Reduction (PCA, LDA)
    3. 5.3. Model Evaluation and Validation
  13. Deep Learning with Rust
  14. 6. Introduction to Deep Learning
    1. 6.1. Neural Networks
    2. 6.2. Convolutional Neural Networks (CNNs)
    3. 6.3. Recurrent Neural Networks (RNNs)
    4. 6.4. Training Deep Learning Models
    5. 6.5. Model Deployment
  15. Advanced Topics
  16. 7. Time Series Analysis
  17. 8. Natural Language Processing (NLP)
  18. 9. Reinforcement Learning
  19. 10. Big Data Processing
  20. Case Studies
  21. 11. Case Study 1: Predictive Analytics
  22. 12. Case Study 2: Image Classification
  23. 13. Case Study 3: Sentiment Analysis
  24. Tools and Libraries
  25. 14. Overview of Rust Libraries for Data Science
  26. 15. Using Polars for DataFrame Operations
  27. 16. Machine Learning with SmartCore
  28. 17. Deep Learning with TensorFlow Rust
  29. Best Practices
  30. 18. Code Optimization
  31. 19. Memory Management
  32. 20. Debugging and Profiling
  33. 21. Documentation and Testing
  34. Conclusion
  35. 22. Summary and Future Directions

Data Science Distilled with Rust

Linear Regression