my solutions to Coursera Machine Learning course, using python
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ex1 - Linear Regression
ex1-oneVariable.ipynb: Linear regression (one variable), gradient descent and scikit learnex1-multipleVariables.ipynb: Linear regression (multiple variable), gradient descent and scikit learn
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ex2 - Logistic Regression
ex2.ipynb: Two parts: Not regularized, Regularized (polynomial features), logistic regression using scikit learn is practiced in assignment 3
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ex3 - Logistic Regression and Neural Networks
ex3-logistic.ipynb: Multi-Class logistic regression (mnist dataset), using gradient descent (Method 1) and scikit learn (Method 2)ex3-neural.ipynb: Implementation of forward-propagation in order to find training accuracy of a given neural network
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ex4 - Neural Networks
ex4.ipynb: A NeuralNetwork class, generalized to whatever network architecture user wants, using fmin_cg to minimize cost function
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ex5 - Regularized Linear Regression and Bias vs. Variance
ex5.ipynb: Linear regression with regularization, learning curves, polynomial linear regression with plots and finding best lambda for our regularization.