Domain Expertise are demonstrated in the python notebooks for below mentioned topics.
Math
- Functions and Differentials
- Maxima and Minima
- Chain Rule
Linear Algebra
- Line Concepts
- Lines and Hyperplanes
- Vector algebra - Magnitude and Directions
- Vector operations
- Matrices
Linear Regression
- Multivariate LR
- Categorical Independent variables
- Root Mean Square Error (RMS)
- Mean Aboslute Error (MAE)
- Theoretical Assumptions
- Stochastic Distrubance Term
- Multi Collinearity
- Heteroscedasticiy of disturbance
- Loss function (Mean Square Loss)
- Gradient Descent
- Regulariation (shrinkage models)
- Lasso_Ridge regression
- Error function (contour graph)
Logistic Regression
- Setting Threshold
- Performance Measures (Precision and Recall)
- Evaluation of Models
- Gain and lift chart
- Concordance and discordance ratio
Classification (KNN and Naive Bayes)
- Naive Bayes
- K-Nearest Neighbor and k value
Support Vector Machine
- SVM Gamma and C
Model performance
- Model performance measures
- ROC and AUC
- ROC Threshold Management for Classification model