- Linear Algebra : eigen values, eigen vectors, PCA, ICA, Pseudo Inverse, Matrix\vector differntiation, Norm
- Central Limit theurom and its importance
- A/B testing, Hypothesis, P-value, F-value, T-test
- Chi-Squared test
- Sampling and its types
- Data cleansing
- Handling Missing data
- Outlier Detection
- Feature Selection Techniques
- Feature engineering
- Expectation, Variance and Mean
- Error analysis
- Bias and variance and its trade-off
- linear/non-lineer/multiple/logistic regression , assumptions, performance metrics
- R-value, Adjusted-R, P-value
- L1 and L2, ridge and lasso
- Ensemble techniques and why its work ?
- Decision tree(CART, ID3.4)
- How to compute Entropy for conntnious data ?
- Bagging, Boosting , Stacking, (why bagging works)
- Underfitting and Overfitting, Tradeoff, overcoming them , preventive methods
- Random Forest
- Adaboost, Gradient Boost, XGboost etc.
- Perceptron model, Activation function, Neural network/cnn, Backpropagation, Grad descent/asscent
- Effect of Batch size, learning rate.
- Loss function/ optimization/ how to derive them
- SVM and assumptions
- conditional probability, conditional idependence
- Naive bayes and assumption
- Confusion Matrix, AUC, ROC, false positive, false negative, etc.
- Performance metrics in CNN (mAP, confusion matrix etc.)
- K- nearest neighbour, Assumption, Performance metrics, Advantages and Disadvantages.
- K- mean, Assumption, Performance metrics, Advantages and Disadvantages.
- DSBCAN and Other clustering algorithm
- Gaussian mixture model
- Expectation maximization
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