ML-AI Garden

� Mathematics and Statistics

The Basics

Statistics: The Basics

Discrete Probability Distributions

• Bernoulli Distribution
• Binomial Distribution
• Poisson Distribution

Continuous Probability Distributions

• Uniform Distribution
• Exponential Distribution
• Beta Distribution
• Gaussian Distribution

Entropy

• Entropy (Shannon Entropy)
• Joint Entropy
• Conditional Entropy
• Mutual Information
• Information Gain

The Specifics

• R-Square
• Residuals
• ANOVA
• P-Value
• Entropy Math
• Covariance, Correlation
• Odds and Log-Odds

🛡 Machine Learning

Feature Engineering

★ Concepts

• Linear vs Non Linear
• Normalization & Standardization

★ Feature Selection

• Approaches to Feature Selection
• Feature Selection vs Elimination

★ Various Approaches of "Feature Selection"

★ Pre-processing

• Feature Transformation & Scaling ★ Main ★
  • Feature Transformation & Scaling - A Step-by-Step Guide
  • Feature Transformation & Scaling - Summary

I. Transformation Techniques

II. Scaling Techniques

• StandardScaler
• RobustScaler
• MinMaxScaler
• MaxAbsScaler
• Mean Normalization

📊 Data Analysis & Visualization

★ Statistical Plots

★ Statistical Tests for Normality

• Skewness and Kurtosis
• Kolmogorov-Smirnov Test
• D'Agostino-Pearson Test
• Jarque-Bera Test
• Shapiro-Wilk Test
• Anderson-Darling Test

Supervised Machine Learning

★ Core Concepts

Optimization

Theory

Functions & Metrics

★ Algorithms

Basic

• Naive Bayes
• Decision Tree
  • Decision Tree - Algo
  • Decision Tree - Balancing

Ensemble Techniques

★ Ensemble Overview ★

1. Bagging

• Random Forest
• Standard Bagging
• Feature Bagging
• Extra Trees
• Pasting
• Random Patches

2. Boosting

• AdaBoost
• Gradient Boosting
• XGBoost
• CatBoost
• LightGBM

3. Stacking

• Blended Stacking
• Stacking with CV
• Restacking
• Weighted Stacking
• Multilayer Stacking
• Hierarchical Stacking

4. Voting

• Voting Types

★ Classification

Evaluation

• Confusion Matrix
  Type I error, Type II error, RoC AUC,
  Precision, Recall, Sensitivity, Specificity, f1_score

★ Regression

Loss Functions

• Regression Loss Functions ★ Main ★
  • Mean Squared Error
  • Root Mean Squared Error
  • Mean Absolute Error
  • Huber Loss
  • Mean Squared Logarithmic Error
  • Mean Absolute Percentage Error
  • Mean Bias Error
  • Log-Cosh Loss

Unsupervised Machine Learning

★ Concepts

• Unsupervised ML
• Dimensionality Reduction

★ Cluster Evaluation

Evaluation Introduction

• Sum of Squared Error
• Silhouette Score
• Dunn Index
• Davies-Bouldin index
• Calinski-Harabasz Index

★ Distance Measures

1. Geometric

• Euclidean Distance
• Manhattan Distance
• Chebyshev Distance
• Minkowski Distance
• Generalized Lp Norm

2. Angular / Similarity-Based

• Cosine Similarity

3. Sets and Sequence

• Jaccard Distance
• Hamming Distance
• Levenshtein Distance

★ Algorithms

• PCA
• K-means

🤖 Deep Learning

• Activation Function
• Universal Approximation Theorem
• Deep Learning — Intro
  • Complete Example

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