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

1. Concepts

• Linear vs Non Linear
• Normalization & Standardization

2. Feature Selection

• Approaches to Feature Selection
• Feature Selection vs Elimination

Various Approaches of "Feature Selection"

3. Pre-processing

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

➛ Transformation Techniques

➛ Scaling Techniques

📊 Data Analysis & Visualization

★ Statistical Plots

★ Statistical Tests for Normality

Supervised Machine Learning

★ Core Concepts

1. Optimization

2. Theory

3. Functions & Metrics

★ Algorithms

1. Basic

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

2. Ensemble Techniques

★ Ensemble Overview ★

1. Bagging

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

2. Boosting

• Gradient Boosting
  • Mathematical Derivation of GBM
• XGBoost
• LightGBM
• AdaBoost
• CatBoost

⭐ Major Boosting Algorithms Comparison

3. Stacking

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

4. Voting

• Voting Types

★ Classification Evaluation

Evaluation

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

Loss Function

Binary Classification

• Binary Cross entropy (BCE)
• Likelihood Loss
• Hing Loss and Squared Hing loss

Multi-Caterorial

• Caterorial Cross entropy (CCE)
• Kullback Leibler Divergence (KLD)

★ Regression Evaluation

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
  Linear, Sigmoid, SoftMax, Tanh, ReLU, Leaky ReLU, ELU
• Universal Approximation Theorem
• Deep Learning — Intro
  • Complete Example
• Optimizers
• Regularization

🖼️ Computer Vision

• Introduction to Computer Vision
• Convolutional Neural Networks (CNN)
  (Stride, Padding, Pooling, Flatten, Filters, Activation Function, Dropouts)
• CNN Output Feature Size and Parameters
• Computer Vision - Filters
  • Smoothing & Blurring Filters
  • Sharpening Filters
  • Edge Detection & Gradient Filters
  • Morphological & Non-Linear Filters

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