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ᯓ ✈︎ Chintan Gandhi

🛡 Machine Learning

Feature engineering

★ Concepts

Linear vs Non Linear
Normalization & Standardization

★ Feature Selection

Approaches to Feature Selection
Feature Selection vs Elimination
ᯓ ✈︎ Various Approaches of "Feature Selection"
Few more will be added soon ➛ Work In Progress
Anova F-Test Information Gain Dispersion Ratio
Mutual Information Pearson Correlation Variance Threshold
Entropy Entropy Math

★ Pre-processing

Feature Transformation & Scaling ★ Main ★
Feature Transformation & Scaling - A Step-by-Step Guide
Feature Transformation & Scaling - Summary
I. Transformation Techniques
Log Transformation Logit Transformation
QuantileTransformer PowerTransformer Polynomial Transformation
Square Transformation (x²) Reciprocal Transformation (1/x) Square Root Transformation (√x)
II. Scaling Techniques
StandardScaler RobustScaler MinMaxScaler MaxAbsScaler

★ Statistical Plots for Data Analysis

📦 Box Plot 🔥 Heatmap 📊 Histogram Plot 🎯 Joint Plot
📈 KDE Plot 📈 Line Plot 📈 LOWESS Plot 🎯 Pair Plot
📉 Q-Q Plot 📌 Scatter Plot 🎻 Violin Plot 📉 Residual Plot
📈 Andrews Curves 🦟 Strip/Swarm Plot

★ Statistical Test for Normal distribution

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

Modeling

★ Concepts

Multicollinearity
Bias, Variance, Overfitting & Underfitting
➢ The Bullseye Target
Derivatives, Partial Derivatives, Gradient
Logit, Sigmoid, Softmax

★ Algorithms

Naive Bayes Decision Tree

★ Classification

Evaluation Matrix

Confusion Matrix

Type I error, Type I error, RoC AUC,
Precision, Recall, Sensitivity, Specificity, f1_score

★ Regression

Loss evaluation
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

🛡 Mathematics and Statistics

The Basics

Statistics: The Basics

Mean, Median, Mode, Range, IQR, Dispersion, Standard Deviation,
Variance, Covariance, Correlation, Standard Error, Z-Score, T-Score

Probability: The Basics
Counting Principles, Permutations, Combinations,
Probability, Conditional Probablility, Law of Total Probability,
Independent Events, Mutually Exclusive Events, Bayes Theorem

Probability Distribution
Random Variables

Discrete Probability Distributions

Continuous Probability Distributions

Entropy

The Specifics

ᯓ ✈︎ About me