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Machine Learning By Prof. Andrew Ng 🌟🌟🌟🌟⭐

This page contains all my YouTube/Coursera Machine Learning courses and resources 📖 by Prof. Andrew Ng 👨

Table of Contents

  1. Brief Intro
  2. Video lectures Index
  3. Programming Exercise Tutorials
  4. Programming Exercise Test Cases
  5. Useful Resources
  6. Schedule
  7. Extra Information
  8. Online E-Books
  9. Aditional Information

Brief Intro

The most of the course talking about hypothesis function and minimising cost funtions

Hypothesis

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 non-spam emails.

Cost Function

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

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)

Differnce between cost function and gradient descent functions

Cost Function Gradient Descent 

            function J = computeCostMulti(X, y, theta)
                m = length(y); % number of training examples
                J = 0;
                predictions =  X*theta;
                sqerrors = (predictions - y).^2;
                J = 1/(2*m)* sum(sqerrors);
            end
            

            function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)    
                m = length(y); % number of training examples
                J_history = zeros(num_iters, 1);
                for iter = 1:num_iters
                    predictions =  X * theta;
                    updates = X' * (predictions - y);
                    theta = theta - alpha * (1/m) * updates;
                    J_history(iter) = computeCostMulti(X, y, theta);
                end
            end

Bias and Variance

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 under-fitting.

Source: http://scott.fortmann-roe.com/docs/BiasVariance.html

Hypotheis and Cost Function Table

Algorithm Hypothesis Function Cost Function Gradient Descent
Linear Regression linear_regression_hypothesis linear_regression_cost
Linear Regression with Multiple variables linear_regression_hypothesis linear_regression_cost linear_regression_multi_var_gradient
Logistic Regression logistic_regression_hypothesis logistic_regression_cost logistic_regression_gradient
Logistic Regression with Multiple Variables logistic_regression_multi_var_cost logistic_regression_multi_var_gradient
Neural Networks nural_cost

Regression with Pictures

Video lectures Index

https://class.coursera.org/ml/lecture/preview

Programming Exercise Tutorials

https://www.coursera.org/learn/machine-learning/discussions/all/threads/m0ZdvjSrEeWddiIAC9pDDA

Programming Exercise Test Cases

https://www.coursera.org/learn/machine-learning/discussions/all/threads/0SxufTSrEeWPACIACw4G5w

Useful Resources

https://www.coursera.org/learn/machine-learning/resources/NrY2G

Schedule:

Week 1:

Week 2:

Week 3:

Week 4:

Week 5:

Week 6:

Week 7:

Week 8:

Week 9:

Week 10:

Week 11:

  • Application example: Photo OCR - pdf - ppt

Extra Information

Online E Books

Aditional Information

Links

Statistics Models

NLP forums