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Supervised Learning (ML system design (Prioritize, error analysis,…
Supervised Learning
ML system design
Prioritize
error analysis
handling skewed data
large data sets
Logistic regression
Classification and representation
classification
hypothesis representation
decision boundary
Model
cost function
Multiclass (1-vs-all)
Neural Networks
Backproagation
Gradient Checking
Random Initialization
Linear Regression
one variable
parameter learning (gradient descent
model and cost function
multiple variable
Multivariante linear regression
Gradient descent for mult
Features & polynomial regression
Computing parameters analytically (normal equation)
Applying ML
Evaluating Learning Algorithm
Eval hypothesis
model selection
train / validation / test sets
Bias vs. variance
Regularization
Learning Curves
Support vector machines (SVM)
large margin classification
Kernels
Regularization
Solving the Problem of Overfitting