Data Science and Machine Learning Algorithms

This page is my personal repository of most common and useful machine learning algorithms using Python and other data science tricks and tips. It is a vast area that can be classified or separated in many different ways

Data Science Algebra

• Eigen Decomposition
  
• Singular Value Decomposition (SVD)

Data Science

Data science involves extracting knowledge from structured and unstructured data. It combines principle from statistics, machine learning, data analysis, and domain knoledge to understand and interpret the data

Data Collection & Accuisition

• Web srcaping: Data collection through Webscraping

Data Cleaning, Pre-processing & Exploratory Data Analysis (EDA)

• Handling Missing Values: Data Transformation.
  
• Feature Engineering
  
• Encoding Categorical Variables
  
• Exploratory Data Analysis (EDA)
  • Descriptive Statistics
    
• Importance of Class Balance

Statistical Methods

• ANOVA - Categorical Features’: How do we treat the categorical features for our data science project?

Machine Learning Algorithms

• Simple Linear Regression
• Multiple Linear Regression
• Logistic Regression
  
• Polynomial Regression
• K-Nearest Neighbor (KNN) Regression and Classification
• Support Vector Machine (SVM)
• Naive Bayes
• Linear Discriminant Analysis (LDA)
  
• Quadratic Discriminant Analysis (QDA)
• Multi-class Classification
• What is Bayesian or Probabilistic Classification?
  
• Linear Discriminant Analysis (LDA)
  
• Ensemble Learning
  • Bagging: Decision Regression and Classification
    
  • Bagging: Random Forest Regression and Classification
    
  • Boosting: Adaptive Boosting
    
• Clustering
  • k-Means Clustering
    
  • Principal Component Analysis

Deep Learning

Artificial Neural Networks (ANN)

• Regression
  
• Classification

Model Evaluation Metrics

• For Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), \(R^2\) score
  
• For Classification: Accuracy, Precision, Recall, F1 Score, ROC-AUC
  
• Cross-validation: kFold, Stratified k-fold, leave-one-out

Model Optimization

• Bias-Variance: Bias Variance Trade off
  
• Hyperparameter Tuning: Grid Search, Random Search, Bayesian Optimization
  
• Features Selection Techniques: Recursive Feature Elimination (RFE), L1 or Rasso Regurlarization, L2 or Ridge Regularization
  
• Model Interpretability: SHAP (Shapley values), LIME (Local Interpretable Model-agnostic Explanations)