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Fine Tuning Data science Algorithm

Fine Tuning Data science Algorithm

Feature Engineering and Model Selection
Underfitting and overfitting
Bias, Variance Trade-off / F1scores
Confusion matrix
Accuracy metrics
Univariate, Bivariate, Multivariate Dataset
Evaluating machine learning model
ROC Curves
Hyper parameter tuning
Importance of Data and its quality
Attributes Types
Feature selection and Feature extraction
Stepwise Selection
Loss Function
Curse of Dimensionality
ChiSquare Test
Impact on Outliers
Cohen’s D Statistics
Error Analysis
General Distance metrics
Graph analysis on Datasets
Regularization
MSE, RMSE, MSE
Feature Slicing
Correlation and Causation
Training /Validation /Testing Data
Learning Rate
Confidence Intervals
Degree of Freedom
Coefficients and Collinearity
P value


1. PCA
2. LDA
3. QDA
Intuition on Dimensionality Reduction
Geometrical intuition.
Alternative formulation of PCA: distance minimization
Eigenvalues and eigenvectors.
PCA for dimensionality reduction and visualization.
Visualize MNIST dataset.
Limitations of PCA
Ts-SNE Estimator for Dimensionality Reduction
Impact on Algorithm


Holdout Method
K-Fold Cross Validation
Stratified K-Fold Cross Validation
Leave-One-Out Cross Validation