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

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

Table of Contents

  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

Breif 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

AlgorithemHypothesis FunctionCost FunctionGradient Descent
Linear Regressionlinear_regression_hypothesislinear_regression_cost
Linear Regression with Multiple variableslinear_regression_hypothesislinear_regression_costlinear_regression_multi_var_gradient
Logistic Regressionlogistic_regression_hypothesislogistic_regression_costlogistic_regression_gradient
Logistic Regression with Multiple Variablelogistic_regression_multi_var_costlogistic_regression_multi_var_gradient
Nural Networksnural_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 - Due 07/16/17:

Week 2 - Due 07/23/17:

Week 3 - Due 07/30/17:

Week 4 - Due 08/06/17:

Week 5 - Due 08/13/17:

Week 6 - Due 08/20/17:

Week 7 - Due 08/27/17:

Week 8 - Due 09/03/17:

Week 9 - Due 09/10/17:

Week 10 - Due 09/17/17:

Week 11 - Due 09/24/17:

  • Application example: Photo OCR - pdf - ppt

Extra Information

Online E Books

Aditional Information

💥 Course Status 👇

coursera_course_completion

Links

Statistics Models

NLP forums

About

Coursera Machine Learning By Prof. Andrew Ng

vkosuri.github.io/CourseraMachineLearning/

Topics

courseracoursera-machine-learningmachine-learning

Resources

Readme
MIT license
Activity

Stars

790 stars

Watchers

11 watching

Forks

311 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
Machine Learning By Prof. Andrew Ng 🌟🌟🌟🌟⭐
This page continas all my coursera machine learning courses and resources 📖 by Prof. Andrew Ng 👨
Table of Contents
  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
Breif 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
AlgorithemHypothesis FunctionCost FunctionGradient Descent
Linear Regressionlinear_regression_hypothesislinear_regression_cost
Linear Regression with Multiple variableslinear_regression_hypothesislinear_regression_costlinear_regression_multi_var_gradient
Logistic Regressionlogistic_regression_hypothesislogistic_regression_costlogistic_regression_gradient
Logistic Regression with Multiple Variablelogistic_regression_multi_var_costlogistic_regression_multi_var_gradient
Nural Networksnural_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 - Due 07/16/17:
Week 2 - Due 07/23/17:
Week 3 - Due 07/30/17:
Week 4 - Due 08/06/17:
Week 5 - Due 08/13/17:
Week 6 - Due 08/20/17:
Week 7 - Due 08/27/17:
Week 8 - Due 09/03/17:
Week 9 - Due 09/10/17:
Week 10 - Due 09/17/17:
Week 11 - Due 09/24/17:
  • Application example: Photo OCR - pdf - ppt
Extra Information
Online E Books
Aditional Information
💥 Course Status 👇
coursera_course_completion
Links
Statistics Models
NLP forums
About
Coursera Machine Learning By Prof. Andrew Ng
vkosuri.github.io/CourseraMachineLearning/
Topics
courseracoursera-machine-learningmachine-learning
Resources
Readme
MIT license
Activity
Stars
790 stars
Watchers
11 watching
Forks
311 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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README.md
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_config.yml
Repository files navigation
Machine Learning By Prof. Andrew Ng 🌟🌟🌟🌟⭐
This page continas all my coursera machine learning courses and resources 📖 by Prof. Andrew Ng 👨
Table of Contents
  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
Breif 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
AlgorithemHypothesis FunctionCost FunctionGradient Descent
Linear Regressionlinear_regression_hypothesislinear_regression_cost
Linear Regression with Multiple variableslinear_regression_hypothesislinear_regression_costlinear_regression_multi_var_gradient
Logistic Regressionlogistic_regression_hypothesislogistic_regression_costlogistic_regression_gradient
Logistic Regression with Multiple Variablelogistic_regression_multi_var_costlogistic_regression_multi_var_gradient
Nural Networksnural_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 - Due 07/16/17:
Week 2 - Due 07/23/17:
Week 3 - Due 07/30/17:
Week 4 - Due 08/06/17:
Week 5 - Due 08/13/17:
Week 6 - Due 08/20/17:
Week 7 - Due 08/27/17:
Week 8 - Due 09/03/17:
Week 9 - Due 09/10/17:
Week 10 - Due 09/17/17:
Week 11 - Due 09/24/17:
  • Application example: Photo OCR - pdf - ppt
Extra Information
Online E Books
Aditional Information
💥 Course Status 👇
coursera_course_completion
Links
Statistics Models
NLP forums
About
Coursera Machine Learning By Prof. Andrew Ng
vkosuri.github.io/CourseraMachineLearning/
Topics
courseracoursera-machine-learningmachine-learning
Resources
Readme
MIT license
Activity
Stars
790 stars
Watchers
11 watching
Forks
311 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Machine Learning By Prof. Andrew Ng 🌟🌟🌟🌟⭐
This page continas all my coursera machine learning courses and resources 📖 by Prof. Andrew Ng 👨
Table of Contents
  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
Breif 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
AlgorithemHypothesis FunctionCost FunctionGradient Descent
Linear Regressionlinear_regression_hypothesislinear_regression_cost
Linear Regression with Multiple variableslinear_regression_hypothesislinear_regression_costlinear_regression_multi_var_gradient
Logistic Regressionlogistic_regression_hypothesislogistic_regression_costlogistic_regression_gradient
Logistic Regression with Multiple Variablelogistic_regression_multi_var_costlogistic_regression_multi_var_gradient
Nural Networksnural_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 - Due 07/16/17:
Week 2 - Due 07/23/17:
Week 3 - Due 07/30/17:
Week 4 - Due 08/06/17:
Week 5 - Due 08/13/17:
Week 6 - Due 08/20/17:
Week 7 - Due 08/27/17:
Week 8 - Due 09/03/17:
Week 9 - Due 09/10/17:
Week 10 - Due 09/17/17:
Week 11 - Due 09/24/17:
  • Application example: Photo OCR - pdf - ppt
Extra Information
Online E Books
Aditional Information
💥 Course Status 👇
coursera_course_completion
Links
Statistics Models
NLP forums
About
Coursera Machine Learning By Prof. Andrew Ng
vkosuri.github.io/CourseraMachineLearning/
Topics
courseracoursera-machine-learningmachine-learning
Resources
Readme
MIT license
Activity
Stars
790 stars
Watchers
11 watching
Forks
311 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Latest commit
History
10 Commits
Folders and files
NameName
Last commit message
Last commit date
extra
extra
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.gitattributes
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LICENSE.md
README.md
README.md
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_config.yml
Repository files navigation
Machine Learning By Prof. Andrew Ng 🌟🌟🌟🌟⭐
This page continas all my coursera machine learning courses and resources 📖 by Prof. Andrew Ng 👨
Table of Contents
  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
Breif 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
AlgorithemHypothesis FunctionCost FunctionGradient Descent
Linear Regressionlinear_regression_hypothesislinear_regression_cost
Linear Regression with Multiple variableslinear_regression_hypothesislinear_regression_costlinear_regression_multi_var_gradient
Logistic Regressionlogistic_regression_hypothesislogistic_regression_costlogistic_regression_gradient
Logistic Regression with Multiple Variablelogistic_regression_multi_var_costlogistic_regression_multi_var_gradient
Nural Networksnural_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 - Due 07/16/17:
Week 2 - Due 07/23/17:
Week 3 - Due 07/30/17:
Week 4 - Due 08/06/17:
Week 5 - Due 08/13/17:
Week 6 - Due 08/20/17:
Week 7 - Due 08/27/17:
Week 8 - Due 09/03/17:
Week 9 - Due 09/10/17:
Week 10 - Due 09/17/17:
Week 11 - Due 09/24/17:
  • Application example: Photo OCR - pdf - ppt
Extra Information
Online E Books
Aditional Information
💥 Course Status 👇
coursera_course_completion
Links
Statistics Models
NLP forums
About
Coursera Machine Learning By Prof. Andrew Ng
vkosuri.github.io/CourseraMachineLearning/
Topics
courseracoursera-machine-learningmachine-learning
Resources
Readme
MIT license
Activity
Stars
790 stars
Watchers
11 watching
Forks
311 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Machine Learning By Prof. Andrew Ng 🌟🌟🌟🌟⭐
This page continas all my coursera machine learning courses and resources 📖 by Prof. Andrew Ng 👨
Table of Contents
  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
Breif 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
AlgorithemHypothesis FunctionCost FunctionGradient Descent
Linear Regressionlinear_regression_hypothesislinear_regression_cost
Linear Regression with Multiple variableslinear_regression_hypothesislinear_regression_costlinear_regression_multi_var_gradient
Logistic Regressionlogistic_regression_hypothesislogistic_regression_costlogistic_regression_gradient
Logistic Regression with Multiple Variablelogistic_regression_multi_var_costlogistic_regression_multi_var_gradient
Nural Networksnural_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 - Due 07/16/17:
Week 2 - Due 07/23/17:
Week 3 - Due 07/30/17:
Week 4 - Due 08/06/17:
Week 5 - Due 08/13/17:
Week 6 - Due 08/20/17:
Week 7 - Due 08/27/17:
Week 8 - Due 09/03/17:
Week 9 - Due 09/10/17:
Week 10 - Due 09/17/17:
Week 11 - Due 09/24/17:
  • Application example: Photo OCR - pdf - ppt
Extra Information
Online E Books
Aditional Information
💥 Course Status 👇
coursera_course_completion
Links
Statistics Models
NLP forums
About
Coursera Machine Learning By Prof. Andrew Ng
vkosuri.github.io/CourseraMachineLearning/
Topics
courseracoursera-machine-learningmachine-learning
Resources
Readme
MIT license
Activity
Stars
790 stars
Watchers
11 watching
Forks
311 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
10 Commits
Folders and files
NameName
Last commit message
Last commit date
extra
extra
home
home
.gitattributes
.gitattributes
.gitignore
.gitignore
LICENSE.md
LICENSE.md
README.md
README.md
_config.yml
_config.yml
Repository files navigation
Machine Learning By Prof. Andrew Ng 🌟🌟🌟🌟⭐
This page continas all my coursera machine learning courses and resources 📖 by Prof. Andrew Ng 👨
Table of Contents
  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
Breif 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
AlgorithemHypothesis FunctionCost FunctionGradient Descent
Linear Regressionlinear_regression_hypothesislinear_regression_cost
Linear Regression with Multiple variableslinear_regression_hypothesislinear_regression_costlinear_regression_multi_var_gradient
Logistic Regressionlogistic_regression_hypothesislogistic_regression_costlogistic_regression_gradient
Logistic Regression with Multiple Variablelogistic_regression_multi_var_costlogistic_regression_multi_var_gradient
Nural Networksnural_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 - Due 07/16/17:
Week 2 - Due 07/23/17:
Week 3 - Due 07/30/17:
Week 4 - Due 08/06/17:
Week 5 - Due 08/13/17:
Week 6 - Due 08/20/17:
Week 7 - Due 08/27/17:
Week 8 - Due 09/03/17:
Week 9 - Due 09/10/17:
Week 10 - Due 09/17/17:
Week 11 - Due 09/24/17:
  • Application example: Photo OCR - pdf - ppt
Extra Information
Online E Books
Aditional Information
💥 Course Status 👇
coursera_course_completion
Links
Statistics Models
NLP forums
About
Coursera Machine Learning By Prof. Andrew Ng
vkosuri.github.io/CourseraMachineLearning/
Topics
courseracoursera-machine-learningmachine-learning
Resources
Readme
MIT license
Activity
Stars
790 stars
Watchers
11 watching
Forks
311 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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10 Commits
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NameName
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Repository files navigation
Machine Learning By Prof. Andrew Ng 🌟🌟🌟🌟⭐
This page continas all my coursera machine learning courses and resources 📖 by Prof. Andrew Ng 👨
Table of Contents
  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
Breif 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
AlgorithemHypothesis FunctionCost FunctionGradient Descent
Linear Regressionlinear_regression_hypothesislinear_regression_cost
Linear Regression with Multiple variableslinear_regression_hypothesislinear_regression_costlinear_regression_multi_var_gradient
Logistic Regressionlogistic_regression_hypothesislogistic_regression_costlogistic_regression_gradient
Logistic Regression with Multiple Variablelogistic_regression_multi_var_costlogistic_regression_multi_var_gradient
Nural Networksnural_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 - Due 07/16/17:
Week 2 - Due 07/23/17:
Week 3 - Due 07/30/17:
Week 4 - Due 08/06/17:
Week 5 - Due 08/13/17:
Week 6 - Due 08/20/17:
Week 7 - Due 08/27/17:
Week 8 - Due 09/03/17:
Week 9 - Due 09/10/17:
Week 10 - Due 09/17/17:
Week 11 - Due 09/24/17:
  • Application example: Photo OCR - pdf - ppt
Extra Information
Online E Books
Aditional Information
💥 Course Status 👇
coursera_course_completion
Links
Statistics Models
NLP forums
About
Coursera Machine Learning By Prof. Andrew Ng
vkosuri.github.io/CourseraMachineLearning/
Topics
courseracoursera-machine-learningmachine-learning
Resources
Readme
MIT license
Activity
Stars
790 stars
Watchers
11 watching
Forks
311 forks
Report repository
Releases
Packages
Contributors
Languages