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MITx

This repo is related to projects of MITx;A machine learning course developed by MIT professors and stuff.

Contains all projects from linear models to deep learning.

Contents

  • Linear Classifiers, Regularization: Sentiment analysis
  • Neural Networks with Pytorch: ANNs, RNNs, CNNs
  • Unsupervised Learning : K-Nearest, Mixture Models and EM algorithm (Gaussian Mixture Models)
  • Reinforcement Learning: Q-Learning algorithm

Tools

you gonna need this following packages:

numpy, matplotlib, scipy, tqdm, scikit-learn , pytorch

visit Pytorch for more installation guide depending in your OS and envs (pip or anaconda) w/ or wo/ CUDA.

Using conda environment

Create your environment

 conda create -n <name of your env> python==3.9

Then activate the env

conda activate <name of your env>

Using pip (or conda), in one go, install these packages

pip install numpy scipy matplotlib tqdm scikit-learn

Note:

This is not a project repo, it's a somewhat a guide for new verified learners to slove the project problems, in case they stuck.

Author : EL Abassi Abderrazaq

About

MITx micromaster class, Machine Learning w/ python, from linear models to deep learning

Topics

deep-learningedxlinear-algebramachine-learningmitmitxnueral-networkspythonpytorchreinforcement-learningscikit-learnstatisticssupervised-learningunsupervised-learning

Resources

Readme
Activity

Stars

1 star

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1 watching

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0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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MITx
This repo is related to projects of MITx;A machine learning course developed by MIT professors and stuff.
Contains all projects from linear models to deep learning.
Contents
  •  Linear Classifiers, Regularization: Sentiment analysis
  •  Neural Networks with Pytorch: ANNs, RNNs, CNNs
  •  Unsupervised Learning : K-Nearest, Mixture Models and EM algorithm (Gaussian Mixture Models)
  •  Reinforcement Learning: Q-Learning algorithm
Tools
you gonna need this following packages:
numpy, matplotlib, scipy, tqdm, scikit-learn , pytorch
visit Pytorch for more installation guide depending in your OS and envs (pip or anaconda) w/ or wo/ CUDA.
Using conda environment
Create your environment
 conda create -n <name of your env> python==3.9

Then activate the env
conda activate <name of your env>

Using pip (or conda), in one go, install these packages
pip install numpy scipy matplotlib tqdm scikit-learn

Note:
This is not a project repo, it's a somewhat a guide for new verified learners to slove the project problems, in case they stuck.
Author : EL Abassi Abderrazaq
About
MITx micromaster class, Machine Learning w/ python, from linear models to deep learning
Topics
deep-learningedxlinear-algebramachine-learningmitmitxnueral-networkspythonpytorchreinforcement-learningscikit-learnstatisticssupervised-learningunsupervised-learning
Resources
Readme
Activity
Stars
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1 watching
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, 'i');
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(function() {
var style = document.createElement('style');
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(function(){
try {
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Repository files navigation
MITx
This repo is related to projects of MITx;A machine learning course developed by MIT professors and stuff.
Contains all projects from linear models to deep learning.
Contents
  •  Linear Classifiers, Regularization: Sentiment analysis
  •  Neural Networks with Pytorch: ANNs, RNNs, CNNs
  •  Unsupervised Learning : K-Nearest, Mixture Models and EM algorithm (Gaussian Mixture Models)
  •  Reinforcement Learning: Q-Learning algorithm
Tools
you gonna need this following packages:
numpy, matplotlib, scipy, tqdm, scikit-learn , pytorch
visit Pytorch for more installation guide depending in your OS and envs (pip or anaconda) w/ or wo/ CUDA.
Using conda environment
Create your environment
 conda create -n <name of your env> python==3.9

Then activate the env
conda activate <name of your env>

Using pip (or conda), in one go, install these packages
pip install numpy scipy matplotlib tqdm scikit-learn

Note:
This is not a project repo, it's a somewhat a guide for new verified learners to slove the project problems, in case they stuck.
Author : EL Abassi Abderrazaq
About
MITx micromaster class, Machine Learning w/ python, from linear models to deep learning
Topics
deep-learningedxlinear-algebramachine-learningmitmitxnueral-networkspythonpytorchreinforcement-learningscikit-learnstatisticssupervised-learningunsupervised-learning
Resources
Readme
Activity
Stars
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, 'i');
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// Highlight search terms from Google/DuckDuckGo/Bing referrer
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
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MITx
This repo is related to projects of MITx;A machine learning course developed by MIT professors and stuff.
Contains all projects from linear models to deep learning.
Contents
  •  Linear Classifiers, Regularization: Sentiment analysis
  •  Neural Networks with Pytorch: ANNs, RNNs, CNNs
  •  Unsupervised Learning : K-Nearest, Mixture Models and EM algorithm (Gaussian Mixture Models)
  •  Reinforcement Learning: Q-Learning algorithm
Tools
you gonna need this following packages:
numpy, matplotlib, scipy, tqdm, scikit-learn , pytorch
visit Pytorch for more installation guide depending in your OS and envs (pip or anaconda) w/ or wo/ CUDA.
Using conda environment
Create your environment
 conda create -n <name of your env> python==3.9

Then activate the env
conda activate <name of your env>

Using pip (or conda), in one go, install these packages
pip install numpy scipy matplotlib tqdm scikit-learn

Note:
This is not a project repo, it's a somewhat a guide for new verified learners to slove the project problems, in case they stuck.
Author : EL Abassi Abderrazaq
About
MITx micromaster class, Machine Learning w/ python, from linear models to deep learning
Topics
deep-learningedxlinear-algebramachine-learningmitmitxnueral-networkspythonpytorchreinforcement-learningscikit-learnstatisticssupervised-learningunsupervised-learning
Resources
Readme
Activity
Stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
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document.querySelectorAll('a[href]').forEach(function(a) {
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mutations.forEach(function(m) {
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
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Repository files navigation
MITx
This repo is related to projects of MITx;A machine learning course developed by MIT professors and stuff.
Contains all projects from linear models to deep learning.
Contents
  •  Linear Classifiers, Regularization: Sentiment analysis
  •  Neural Networks with Pytorch: ANNs, RNNs, CNNs
  •  Unsupervised Learning : K-Nearest, Mixture Models and EM algorithm (Gaussian Mixture Models)
  •  Reinforcement Learning: Q-Learning algorithm
Tools
you gonna need this following packages:
numpy, matplotlib, scipy, tqdm, scikit-learn , pytorch
visit Pytorch for more installation guide depending in your OS and envs (pip or anaconda) w/ or wo/ CUDA.
Using conda environment
Create your environment
 conda create -n <name of your env> python==3.9

Then activate the env
conda activate <name of your env>

Using pip (or conda), in one go, install these packages
pip install numpy scipy matplotlib tqdm scikit-learn

Note:
This is not a project repo, it's a somewhat a guide for new verified learners to slove the project problems, in case they stuck.
Author : EL Abassi Abderrazaq
About
MITx micromaster class, Machine Learning w/ python, from linear models to deep learning
Topics
deep-learningedxlinear-algebramachine-learningmitmitxnueral-networkspythonpytorchreinforcement-learningscikit-learnstatisticssupervised-learningunsupervised-learning
Resources
Readme
Activity
Stars
1 star
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1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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}, 1000);
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var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
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(function(){
try {
var __m = "*";
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Repository files navigation
MITx
This repo is related to projects of MITx;A machine learning course developed by MIT professors and stuff.
Contains all projects from linear models to deep learning.
Contents
  •  Linear Classifiers, Regularization: Sentiment analysis
  •  Neural Networks with Pytorch: ANNs, RNNs, CNNs
  •  Unsupervised Learning : K-Nearest, Mixture Models and EM algorithm (Gaussian Mixture Models)
  •  Reinforcement Learning: Q-Learning algorithm
Tools
you gonna need this following packages:
numpy, matplotlib, scipy, tqdm, scikit-learn , pytorch
visit Pytorch for more installation guide depending in your OS and envs (pip or anaconda) w/ or wo/ CUDA.
Using conda environment
Create your environment
 conda create -n <name of your env> python==3.9

Then activate the env
conda activate <name of your env>

Using pip (or conda), in one go, install these packages
pip install numpy scipy matplotlib tqdm scikit-learn

Note:
This is not a project repo, it's a somewhat a guide for new verified learners to slove the project problems, in case they stuck.
Author : EL Abassi Abderrazaq
About
MITx micromaster class, Machine Learning w/ python, from linear models to deep learning
Topics
deep-learningedxlinear-algebramachine-learningmitmitxnueral-networkspythonpytorchreinforcement-learningscikit-learnstatisticssupervised-learningunsupervised-learning
Resources
Readme
Activity
Stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - Abdoelabassi/MITx-MLtoDLwPython: MITx micromaster class, Machine Learning w/ python, from linear models to deep learning · GitHub
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Repository files navigation
MITx
This repo is related to projects of MITx;A machine learning course developed by MIT professors and stuff.
Contains all projects from linear models to deep learning.
Contents
  •  Linear Classifiers, Regularization: Sentiment analysis
  •  Neural Networks with Pytorch: ANNs, RNNs, CNNs
  •  Unsupervised Learning : K-Nearest, Mixture Models and EM algorithm (Gaussian Mixture Models)
  •  Reinforcement Learning: Q-Learning algorithm
Tools
you gonna need this following packages:
numpy, matplotlib, scipy, tqdm, scikit-learn , pytorch
visit Pytorch for more installation guide depending in your OS and envs (pip or anaconda) w/ or wo/ CUDA.
Using conda environment
Create your environment
 conda create -n <name of your env> python==3.9

Then activate the env
conda activate <name of your env>

Using pip (or conda), in one go, install these packages
pip install numpy scipy matplotlib tqdm scikit-learn

Note:
This is not a project repo, it's a somewhat a guide for new verified learners to slove the project problems, in case they stuck.
Author : EL Abassi Abderrazaq
About
MITx micromaster class, Machine Learning w/ python, from linear models to deep learning
Topics
deep-learningedxlinear-algebramachine-learningmitmitxnueral-networkspythonpytorchreinforcement-learningscikit-learnstatisticssupervised-learningunsupervised-learning
Resources
Readme
Activity
Stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
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[data-theme="light"], [data-theme="light"],
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.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
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MITx
This repo is related to projects of MITx;A machine learning course developed by MIT professors and stuff.
Contains all projects from linear models to deep learning.
Contents
  •  Linear Classifiers, Regularization: Sentiment analysis
  •  Neural Networks with Pytorch: ANNs, RNNs, CNNs
  •  Unsupervised Learning : K-Nearest, Mixture Models and EM algorithm (Gaussian Mixture Models)
  •  Reinforcement Learning: Q-Learning algorithm
Tools
you gonna need this following packages:
numpy, matplotlib, scipy, tqdm, scikit-learn , pytorch
visit Pytorch for more installation guide depending in your OS and envs (pip or anaconda) w/ or wo/ CUDA.
Using conda environment
Create your environment
 conda create -n <name of your env> python==3.9

Then activate the env
conda activate <name of your env>

Using pip (or conda), in one go, install these packages
pip install numpy scipy matplotlib tqdm scikit-learn

Note:
This is not a project repo, it's a somewhat a guide for new verified learners to slove the project problems, in case they stuck.
Author : EL Abassi Abderrazaq
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MITx micromaster class, Machine Learning w/ python, from linear models to deep learning
Topics
deep-learningedxlinear-algebramachine-learningmitmitxnueral-networkspythonpytorchreinforcement-learningscikit-learnstatisticssupervised-learningunsupervised-learning
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