Coursera Machine Learning By Prof. Andrew Ng
This page continas all my coursera machine learning courses and resources :book: by Prof. Andrew Ng :man:
The most of the course talking about hypothesis function and minimising cost funtions
A hypothesis is a certain function that we believe (or hope) is similar to the true function, the target function that we want to model. In context of email spam classification, it would be the rule we came up with that allows us to separate spam from nonspam emails.
The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. The closer our hypothesis matches the training examples, the smaller the value of the cost function. Theoretically, we would like J(θ)=0
Gradient descent is an iterative minimization method. The gradient of the error function always shows in the direction of the steepest ascent of the error function. Thus, we can start with a random weight vector and subsequently follow the negative gradient (using a learning rate alpha)
Cost Function  Gradient Descent 



When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to "bias" and error due to "variance". There is a tradeoff between a model's ability to minimize bias and variance. Understanding these two types of error can help us diagnose model results and avoid the mistake of over or underfitting.
Source: http://scott.fortmannroe.com/docs/BiasVariance.html
 Algorithem  Hypothesis Function  Cost Function  Gradient Descent        Linear Regression      Linear Regression with Multiple variables      Logistic Regression      Logistic Regression with Multiple Variable      Nural Networks     
https://class.coursera.org/ml/lecture/preview
https://www.coursera.org/learn/machinelearning/discussions/all/threads/m0ZdvjSrEeWddiIAC9pDDA
https://www.coursera.org/learn/machinelearning/discussions/all/threads/0SxufTSrEeWPACIACw4G5w
https://www.coursera.org/learn/machinelearning/resources/NrY2G