Relationship in Data : Covariance - Part 1

Relationship in Data : Covariance - Part 2

Relationship in Data : Pearson’s Correlation - Part 1

Relationship in Data : Pearson’s Correlation - Part 2

Relationship in Data : R-squared

Linear Regression : Least Square Method

Linear Regression : Simple Linear Regression

Linear Regression : Multiple Regression

Linear Regression : General Linear Models (GLM) Unlocked (t-test and ANOVA)

Linear Regression Case Study : Simple Linear Regression

Linear Regression Case Study : t tests

Linear Regression Model : Assumptions

Linear Regression Model : Model Selection using Mallow’ Cp

Linear Regression Model : Backward and Forward Selection

Linear Regression Model : Residual Analysis

Linear Regression Model : Influential Observations Analysis

Linear Regression Model : Collinearity Diagnostics

Linear Regression Case Study 2 : Multiple_Linear_Regression.ipynb

Interview & CV - Guidance

Logistics Regression : Expected Values - Discrete

Logistics Regression : Expected Values - Continuous

Logistics Regression : Test of Association

Logistics Regression : Fisher's Exact Test

Logistics Regression : The Mantel-Haenszel Test (Controlling for Confounders)

Logistics Regression : Logs

Logistics Regression : Odds and Log(Odds)

Logistics Regression : Odds Ratios and Log(Odds Ratios)

Logistics Regression : Probability vs Likelihood

Logistics Regression : Maximum Likelihood

Logistics Regression : Maximum Likelihood - Binomial Distribution

Logistics Regression : Concept

Logistics Regression : Coefficients - Continuous

Logistics Regression : Coefficients - Discrete

Logistics Regression : Reference Cell and Effect Cell Coding

Logistics Regression : R Squared and p-value

Logistics Regression : Concordant and Discordant Pairs

Logistics Regression : Null Deviance and Residual deviance

Logistics Regression : FAIQs

Case Study 3 - Logistics Regression : Real World Case Study

Model Validation : Cross Validation

Model Performance : Confusion Matrix

Model Metrics : Sensitivity, Specificity, Precision and Accuracy

Model Diagnostics : Bias and Variance

Model Evaluation : ROC and AUC

Information Logic : Entropy

Information Logic : Mutual Information

Regularization : Ridge Regression

Regularization : Lasso Regression

Regularization : Elastic-Net Regression

Regularization : Case Study 4

Dimension Reduction : PCA (Principal Component Analysis)

Dimension Reduction : MDS (Multidimensional Scaling) and PCoA (Principal Coordinate Analysis)

Dimension Reduction : t-SNE

Dimension Reduction : UMAP Dimension Reduction

Dimension Reduction : Heatmap

Case Study 5

Clustering : K-Means

Clustering : DBSCAN

Clustering : Hierarchical

Gradient Descent

Stochastic Gradient Descent

K - Nearest Neighbors

Naive Bayes

Gaussian Naive Bayes

Decision Tree : One-Hot, Label, Target and K-Fold Target Encoding

Decision Tree : Classification Tree

Decision Tree : Regression Trees

Decision Tree : Pruning

Case Study 6

Random Forest : Build, use and validate

Random Forest : Missing Data and Clustering

Case Study 7

Boosting : AdaBoost

Boosting : Gradient Boost

Boosting : XGBoost

Boosting : Cosine Similarity

Boosting : CatBoost

Case Study 8

Support Vector Machine : Understanding

Support Vector Machine : The Polynomial Kernel

Support Vector Machine : The Radial Kernel (RBF)