General Machine Learning Notes

A Sphinx Book Theme site with so that all of my Machine learning practice notes are available from anywhere.

General:

• Classification Methods
  • k-Nearest Neighbors (knn)
• Metrics
  • Basics
  • Goodness of Fit metrics

DataCamp:

• Supervised Learning with scikit-learn
  • Supervised Learning Basics
    • Types
    • Terminology
    • Data Prerequisites
    • scikit-learn Syntax
• Intro to Classification
  • k-Nearest Neighbors
    • The Churn Dataset
    • Choose Data
    • Fit
    • Predict
    • Measuring Model Performance (train-test-split)
    • Model Complexity (Overfitting/Underfitting)
• Intro to Regression
• DataCamp Notes
  • Extracting DataFrames from DataCamp

ML Mastery:

• Machine Learning Mastery Python Mini Course
  • Creating DataFrames
    • Create a simple DataFrame
    • Create DataFrame from CSV URL
  • Examining the Data
    • Describe
    • Scatter Plot Matrix
    • Histogram
    • Box Plot
  • Pre-Processing Data
    • Standardize (Scale and Center)
    • Resampling
    • K-Fold Cross Validation
  • Evaluation Metrics
    • Logloss
    • Confusion Matrix
    • Classification Report
    • RMSE and R-Squared
  • Spot-Check Algorithms
    • Linear Algorithms
    • Non-Linear Algorithms
    • Ensemble Algorithms
  • Model Comparison and Selection
  • Improving Model Accuracy
    • Parameter Tuning
    • Ensemble Predictions
  • Finalize and Save

Sphinx:

• Example Markdown File
  • Sphinx Placeholder
  • Mermaid Example
• Example Jupyter Notebook
  • Python Cell Tests
  • Mermaid Diagrams
• Example reStructuredText File
  • Subtitle