This page continas all my coursera machine learning courses and resources 📖 by Prof. Andrew Ng 👨

1. Breif 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

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 non-spam 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)

            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
            

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

Algorithem	Hypothesis Function	Cost Function	Gradient Descent
Linear Regression
Linear Regression with Multiple variables
Logistic Regression
Logistic Regression with Multiple Variable
Nural Networks

• Linear Regression
• Logistic Regression

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

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

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

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

• Welcome - pdf - ppt
• Linear regression with one variable - pdf - ppt
• Linear Algebra review (Optional) - pdf - ppt
• Lecture Notes
• Errata

• Linear regression with multiple variables - pdf - ppt
• Octave tutorial pdf
• Programming Exercise 1: Linear Regression - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

• Logistic regression - pdf - ppt
• Regularization - pdf - ppt
• Programming Exercise 2: Logistic Regression - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

• Neural Networks: Representation - pdf - ppt
• Programming Exercise 3: Multi-class Classification and Neural Networks - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

• Neural Networks: Learning - pdf - ppt
• Programming Exercise 4: Neural Networks Learning - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

• Advice for applying machine learning - pdf - ppt
• Machine learning system design - pdf - ppt
• Programming Exercise 5: Regularized Linear Regression and Bias v.s. Variance - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

• Support vector machines - pdf - ppt
• Programming Exercise 6: Support Vector Machines - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

• Clustering - pdf - ppt
• Dimensionality reduction - pdf - ppt
• Programming Exercise 7: K-means Clustering and Principal Component Analysis - pdf - Problems - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

• Anomaly Detection - pdf - ppt
• Recommender Systems - pdf - ppt
• Programming Exercise 8: Anomaly Detection and Recommender Systems - pdf - Problems - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

• Large scale machine learning - pdf - ppt
• Lecture Notes

• Application example: Photo OCR - pdf - ppt

• Linear Algebra Review and Reference Zico Kolter
• CS229 Lecture notes
• CS229 Problems
• Financial time series forecasting with machine learning techniques
• Octave Examples

• Introduction to Machine Learning by Nils J. Nilsson
• Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan
• Introduction to Data Science by Jeffrey Stanton
• Bayesian Reasoning and Machine Learning by David Barber
• Understanding Machine Learning, © 2014 by Shai Shalev-Shwartz and Shai Ben-David
• Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman
• Pattern Recognition and Machine Learning, by Christopher M. Bishop

• What are the top 10 problems in deep learning for 2017?
• When will the deep learning bubble burst?

• HMM - Hidden Markov Model
• CRFs - Conditional Random Fields
• LSI - Latent Semantic Indexing
• MRF - Markov Random Fields

• SIGIR - Special Interest Group on Information Retrieval
• ACL - Association for Computational Linguistics
• NAACL - The North American Chapter of the Association for Computational Linguistics
• EMNLP - Empirical Methods in Natural Language Processing
• NIPS - Neural Information Processing Systems